{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T22:47:54Z","timestamp":1780440474429,"version":"3.54.1"},"reference-count":157,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T00:00:00Z","timestamp":1608681600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T00:00:00Z","timestamp":1608681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005713","name":"Technische Universit\u00e4t M\u00fcnchen","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005713","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present<jats:italic>MOTChallenge<\/jats:italic>, a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i)<jats:italic>MOT15<\/jats:italic>, along with numerous state-of-the-art results that were submitted in the last years, (ii)<jats:italic>MOT16<\/jats:italic>, which contains new challenging videos, and (iii)<jats:italic>MOT17<\/jats:italic>, that extends<jats:italic>MOT16<\/jats:italic>sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.<\/jats:p>","DOI":"10.1007\/s11263-020-01393-0","type":"journal-article","created":{"date-parts":[[2020,12,23]],"date-time":"2020-12-23T07:03:20Z","timestamp":1608707000000},"page":"845-881","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":297,"title":["MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking"],"prefix":"10.1007","volume":"129","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4623-8749","authenticated-orcid":false,"given":"Patrick","family":"Dendorfer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aljos\u0306a","family":"Os\u0306ep","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anton","family":"Milan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konrad","family":"Schindler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Cremers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian","family":"Reid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Roth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Leal-Taix\u00e9","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,12,23]]},"reference":[{"key":"1393_CR1","doi-asserted-by":"crossref","unstructured":"Alahi, A., Ramanathan, V., & Fei-Fei, L. (2014). Socially-aware large-scale crowd forecasting. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2014.283"},{"key":"1393_CR2","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Roth, S., & Schiele, B. (2010). Monocular 3D pose estimation and tracking by detection. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2010.5540156"},{"key":"1393_CR3","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Iqbal, U., Insafutdinov, E., Pishchulin, L., Milan, A., Gall, J., & Schiele, B. (2018). Posetrack: A benchmark for human pose estimation and tracking. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2018.00542"},{"key":"1393_CR4","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2019.08.008","volume":"368","author":"M Babaee","year":"2019","unstructured":"Babaee, M., Li, Z., & Rigoll, G. (2019). A dual CNN-RNN for multiple people tracking. Neurocomputing, 368, 69\u201383.","journal-title":"Neurocomputing"},{"key":"1393_CR5","doi-asserted-by":"crossref","unstructured":"Bae, S.-H., & Yoon, K.-J. (2014). Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2014.159"},{"issue":"3","key":"1393_CR6","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1109\/TPAMI.2017.2691769","volume":"40","author":"S-H Bae","year":"2018","unstructured":"Bae, S.-H., & Yoon, K.-J. (2018). Confidence-based data association and discriminative deep appearance learning for robust online multi-object tracking. Transactions on Pattern Analysis and Machine Intelligence, 40(3), 595\u2013610.","journal-title":"Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1393_CR7","doi-asserted-by":"crossref","unstructured":"Baisa, N. L. (2018). Online multi-target visual tracking using a HISP filter. In International joint conference on computer vision, imaging and computer graphics theory and applications.","DOI":"10.5220\/0006564504290438"},{"key":"1393_CR8","doi-asserted-by":"crossref","unstructured":"Baisa, N. L. (2019a). Online multi-object visual tracking using a GM-PHD filter with deep appearance learning. In International conference on information fusion.","DOI":"10.5220\/0006564504290438"},{"key":"1393_CR9","doi-asserted-by":"crossref","unstructured":"Baisa, N. L. (2019b). Occlusion-robust online multi-object visual tracking using a GM-PHD filter with a CNN-based re-identification. arXiv preprint arXiv:1912.05949.","DOI":"10.5220\/0006564504290438"},{"key":"1393_CR10","doi-asserted-by":"crossref","unstructured":"Baisa, N. L. (2019c). Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning. arXiv preprint arXiv:1908.03945.","DOI":"10.5220\/0006564504290438"},{"key":"1393_CR11","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.jvcir.2019.01.026","volume":"59","author":"NL Baisa","year":"2019","unstructured":"Baisa, N. L., & Wallace, A. (2019). Development of a n-type GM-PHD filter for multiple target, multiple type visual tracking. Journal of Visual Communication and Image Representation, 59, 257\u2013271.","journal-title":"Journal of Visual Communication and Image Representation"},{"issue":"1","key":"1393_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-010-0390-2","volume":"92","author":"S Baker","year":"2011","unstructured":"Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011). A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92(1), 1\u201331.","journal-title":"International Journal of Computer Vision"},{"key":"1393_CR13","doi-asserted-by":"crossref","unstructured":"Ban, Y., Ba, S., Alameda-Pineda, X., & Horaud, R. (2016). Tracking multiple persons based on a variational Bayesian model. In European conference on computer vision workshops.","DOI":"10.1007\/978-3-319-48881-3_5"},{"key":"1393_CR14","unstructured":"Battaglia, P., Pascanu, R., Lai, M., Rezende, D. J., et\u00a0al. (2016). Interaction networks for learning about objects, relations and physics. In Advances in neural information processing systems."},{"key":"1393_CR15","doi-asserted-by":"crossref","unstructured":"Benfold, B., & Reid, I. (2011). Unsupervised learning of a scene-specific coarse gaze estimator. In International conference on computer vision.","DOI":"10.1109\/ICCV.2011.6126516"},{"key":"1393_CR16","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Meinhardt, T., & Leal-Taix\u00e9, L. (2019). Tracking without bells and whistles. In International conference on computer vision.","DOI":"10.1109\/ICCV.2019.00103"},{"key":"1393_CR17","doi-asserted-by":"publisher","DOI":"10.1155\/2008\/246309","author":"K Bernardin","year":"2008","unstructured":"Bernardin, K., & Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: The CLEAR MOT metrics. Image and Video Processing,. https:\/\/doi.org\/10.1155\/2008\/246309.","journal-title":"Image and Video Processing"},{"key":"1393_CR18","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016a). Simple online and realtime tracking. In International conference on image processing.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"1393_CR19","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ott, L., Ramos, F., & Upcroft, B. (2016b). Alextrac: Affinity learning by exploring temporal reinforcement within association chains. In International conference on robotics and automation.","DOI":"10.1109\/ICRA.2016.7487371"},{"key":"1393_CR20","doi-asserted-by":"crossref","unstructured":"Bochinski, E., Eiselein, V., & Sikora, T. (2017). High-speed tracking-by-detection without using image information. In International conference on advanced video and signal based surveillance.","DOI":"10.1109\/AVSS.2017.8078516"},{"key":"1393_CR21","doi-asserted-by":"crossref","unstructured":"Boragule, A., & Jeon, M. (2017). Joint cost minimization for multi-object tracking. International conference on advanced video and signal based surveillance.","DOI":"10.1109\/AVSS.2017.8078481"},{"key":"1393_CR22","doi-asserted-by":"crossref","unstructured":"Bras\u00f3, G., & Leal-Taix\u00e9, L. (2020). Learning a neural solver for multiple object tracking. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR42600.2020.00628"},{"key":"1393_CR23","doi-asserted-by":"crossref","unstructured":"Chang, M.-F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., & Hays, J. (2019). Argoverse: 3D tracking and forecasting with rich maps. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2019.00895"},{"key":"1393_CR24","doi-asserted-by":"crossref","unstructured":"Chen, J., Sheng, H., Zhang, Y., & Xiong, Z. (2017a). Enhancing detection model for multiple hypothesis tracking. In Conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPRW.2017.266"},{"issue":"11","key":"1393_CR25","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1109\/LSP.2019.2940922","volume":"26","author":"L Chen","year":"2019","unstructured":"Chen, L., Ai, H., Chen, R., & Zhuang, Z. (2019). Aggregate tracklet appearance features for multi-object tracking. Signal Processing Letters, 26(11), 1613\u20131617.","journal-title":"Signal Processing Letters"},{"key":"1393_CR26","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, X., Zhang, J., & Huang, K. (2017b). Beyond triplet loss: A deep quadruplet network for person re-identification. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2017.145"},{"key":"1393_CR27","doi-asserted-by":"crossref","unstructured":"Choi, W. (2015). Near-online multi-target tracking with aggregated local flow descriptor. In International conference on computer vision.","DOI":"10.1109\/ICCV.2015.347"},{"key":"1393_CR28","doi-asserted-by":"crossref","unstructured":"Chu, P., Fan, H., Tan, C. C., & Ling, H. (2019). Online multi-object tracking with instance-aware tracker and dynamic model refreshment. In Winter conference on applications of computer vision.","DOI":"10.1109\/WACV.2019.00023"},{"key":"1393_CR29","doi-asserted-by":"crossref","unstructured":"Chu, P., & Ling, H. (2019). FAMNet: Joint learning of feature, affinity and multi-dimensional assignment for online multiple object tracking. In International conference on computer vision.","DOI":"10.1109\/ICCV.2019.00627"},{"key":"1393_CR30","doi-asserted-by":"crossref","unstructured":"Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., & Yu, N. (2017). Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism. In International conference on computer vision.","DOI":"10.1109\/ICCV.2017.518"},{"key":"1393_CR31","doi-asserted-by":"crossref","unstructured":"Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In Conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPR.2005.177"},{"key":"1393_CR32","doi-asserted-by":"crossref","unstructured":"Dave, A., Khurana, T., Tokmakov, P., Schmid, C., & Ramanan, D. (2020) Tao: A large-scale benchmark for tracking any object. In European conference on computer vision.","DOI":"10.1007\/978-3-030-58558-7_26"},{"key":"1393_CR33","doi-asserted-by":"crossref","unstructured":"Dehghan, A., Assari, S. M., & Shah, M. (2015) GMMCP-tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking. In Conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPR.2015.7299036"},{"key":"1393_CR34","unstructured":"Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., & Leal-Taixe, L. (2019). Cvpr19 tracking and detection challenge: How crowded can it get? arXiv preprint arXiv:1906.04567."},{"key":"1393_CR35","unstructured":"Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., & Leal-Taix\u00e9, L. (2020). MOT20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003."},{"key":"1393_CR36","doi-asserted-by":"crossref","unstructured":"Dicle, C., Camps, O., & Sznaier, M. (2013) The way they move: Tracking targets with similar appearance. In International conference on computer vision.","DOI":"10.1109\/ICCV.2013.286"},{"issue":"8","key":"1393_CR37","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TPAMI.2014.2300479","volume":"36","author":"P Doll\u00e1r","year":"2014","unstructured":"Doll\u00e1r, P., Appel, R., Belongie, S., & Perona, P. (2014). Fast feature pyramids for object detection. Transactions on Pattern Analysis and Machine Intelligence, 36(8), 1532\u20131545.","journal-title":"Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1393_CR38","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Wojek, C., Schiele, B., & Perona, P. (2009) Pedestrian detection: A benchmark. In Conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPRW.2009.5206631"},{"key":"1393_CR39","doi-asserted-by":"crossref","unstructured":"Eiselein, V., Arp, D., P\u00e4tzold, M., & Sikora, T. (2012). Real-time multi-human tracking using a probability hypothesis density filter and multiple detectors. In International conference on advanced video and signal-based surveillance.","DOI":"10.1109\/AVSS.2012.59"},{"key":"1393_CR40","doi-asserted-by":"crossref","unstructured":"Ess, A., Leibe, B., Schindler, K., & Van Gool, L. (2008). A mobile vision system for robust multi-person tracking. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2008.4587581"},{"issue":"1","key":"1393_CR41","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2015). The Pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1), 98\u2013136.","journal-title":"International Journal of Computer Vision"},{"key":"1393_CR42","doi-asserted-by":"crossref","unstructured":"Fagot-Bouquet, L., Audigier, R., Dhome, Y., & Lerasle, F. (2015). Online multi-person tracking based on global sparse collaborative representations. In International conference on image processing.","DOI":"10.1109\/ICIP.2015.7351235"},{"key":"1393_CR43","doi-asserted-by":"crossref","unstructured":"Fagot-Bouquet, L., Audigier, R., Dhome, Y., & Lerasle, F. (2016). Improving multi-frame data association with sparse representations for robust near-online multi-object tracking. In European conference on computer vision workshops.","DOI":"10.1007\/978-3-319-46484-8_47"},{"key":"1393_CR44","doi-asserted-by":"crossref","unstructured":"Fang, K., Xiang, Y., Li, X., & Savarese, S. (2018). Recurrent autoregressive networks for online multi-object tracking. In Winter conference on applications of computer vision.","DOI":"10.1109\/WACV.2018.00057"},{"key":"1393_CR45","doi-asserted-by":"crossref","unstructured":"Felzenszwalb, P. F., & Huttenlocher, D. P. (2006) Efficient belief propagation for early vision. In Conference on computer vision and pattern recognition.","DOI":"10.1007\/s11263-006-7899-4"},{"key":"1393_CR46","doi-asserted-by":"crossref","unstructured":"Ferryman, J., & Ellis, A. (2010) PETS2010: Dataset and challenge. In International conference on advanced video and signal based surveillance.","DOI":"10.1109\/AVSS.2010.90"},{"key":"1393_CR47","doi-asserted-by":"crossref","unstructured":"Ferryman, J., & Shahrokni, A. (2009). PETS2009: Dataset and challenge. In International workshop on performance evaluation of tracking and surveillance.","DOI":"10.1109\/PETS-WINTER.2009.5399556"},{"issue":"9","key":"1393_CR48","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1109\/TMM.2019.2902480","volume":"21","author":"Z Fu","year":"2019","unstructured":"Fu, Z., Angelini, F., Chambers, J., & Naqvi, S. M. (2019). Multi-level cooperative fusion of GM-PHD filters for online multiple human tracking. Transactions on Multimedia, 21(9), 2277\u20132291.","journal-title":"Transactions on Multimedia"},{"key":"1393_CR49","doi-asserted-by":"publisher","first-page":"14764","DOI":"10.1109\/ACCESS.2018.2816805","volume":"6","author":"Z Fu","year":"2018","unstructured":"Fu, Z., Feng, P., Angelini, F., Chambers, J. A., & Naqvi, S. M. (2018). Particle PHD filter based multiple human tracking using online group-structured dictionary learning. Access, 6, 14764\u201314778.","journal-title":"Access"},{"issue":"5","key":"1393_CR50","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1109\/TPAMI.2013.185","volume":"36","author":"A Geiger","year":"2014","unstructured":"Geiger, A., Lauer, M., Wojek, C., Stiller, C., & Urtasun, R. (2014). 3D traffic scene understanding from movable platforms. Transactions on Pattern Analysis and Machine Intelligence, 36(5), 1012\u20131025.","journal-title":"Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1393_CR51","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., & Urtasun, R. (2012) Are we ready for autonomous driving? The KITTI vision benchmark suite. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"1393_CR52","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. In International conference on computer vision.","DOI":"10.1109\/ICCV.2015.169"},{"key":"1393_CR53","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., & LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2006.100"},{"key":"1393_CR54","doi-asserted-by":"crossref","unstructured":"Held, D., Thrun, S., & Savarese, S. (2016). Learning to track at 100 fps with deep regression networks. In European conference on computer vision.","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"1393_CR55","doi-asserted-by":"crossref","unstructured":"Henriques, J. a., Caseiro, R., & Batista, J. (2011). Globally optimal solution to multi-object tracking with merged measurements. In International conference on computer vision.","DOI":"10.1109\/ICCV.2011.6126532"},{"key":"1393_CR56","doi-asserted-by":"crossref","unstructured":"Henschel, R., Leal-Taix\u00e9, L., Cremers, D., & Rosenhahn, B. (2018). Fusion of head and full-body detectors for multi-object tracking. In Conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPRW.2018.00192"},{"key":"1393_CR57","doi-asserted-by":"crossref","unstructured":"Henschel, R., Zou, Y., & Rosenhahn, B. (2019). Multiple people tracking using body and joint detections. In Conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPRW.2019.00105"},{"key":"1393_CR58","unstructured":"Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachussetts, Amherst."},{"issue":"2","key":"1393_CR59","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1364\/JOSAA.34.000280","volume":"34","author":"J Ju","year":"2017","unstructured":"Ju, J., Kim, D., Ku, B., Han, D., & Ko, H. (2017a). Online multi-object tracking with efficient track drift and fragmentation handling. Journal of the Optical Society of America A, 34(2), 280\u2013293.","journal-title":"Journal of the Optical Society of America A"},{"issue":"1","key":"1393_CR60","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1049\/iet-cvi.2016.0068","volume":"11","author":"J Ju","year":"2017","unstructured":"Ju, J., Kim, D., Ku, B., Han, D. K., & Ko, H. (2017b). Online multi-person tracking with two-stage data association and online appearance model learning. IET Computer Vision, 11(1), 87\u201395.","journal-title":"IET Computer Vision"},{"key":"1393_CR61","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2932301","author":"H Karunasekera","year":"2019","unstructured":"Karunasekera, H., Wang, H., & Zhang, H. (2019). Multiple object tracking with attention to appearance, structure, motion and size. Access,. https:\/\/doi.org\/10.1109\/ACCESS.2019.2932301.","journal-title":"Access"},{"key":"1393_CR62","unstructured":"Kesten, R., Usman, M., Houston, J., Pandya, T., Nadhamuni, K., et\u00a0al. (2019) Lyft level 5 av dataset 2019. https:\/\/level5.lyft.com\/dataset\/."},{"key":"1393_CR63","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2876253","author":"M Keuper","year":"2018","unstructured":"Keuper, M., Tang, S., Andres, B., Brox, T., & Schiele, B. (2018). Motion segmentation and multiple object tracking by correlation co-clustering. Transactions on Pattern Analysis and Machine Intelligence,. https:\/\/doi.org\/10.1109\/TPAMI.2018.2876253.","journal-title":"Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1393_CR64","doi-asserted-by":"crossref","unstructured":"Kieritz, H., Becker, S., H\u00e4bner, W., & Arens, M. (2016). Online multi-person tracking using integral channel features. In International conference on advanced video and signal based surveillance.","DOI":"10.1109\/AVSS.2016.7738059"},{"key":"1393_CR65","doi-asserted-by":"crossref","unstructured":"Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015). Multiple hypothesis tracking revisited. In International conference on computer vision.","DOI":"10.1109\/ICCV.2015.533"},{"key":"1393_CR66","doi-asserted-by":"crossref","unstructured":"Kim, C., Li, F., & Rehg, J. M. (2018). Multi-object tracking with neural gating using bilinear LSTM. In European conference on computer vision.","DOI":"10.1007\/978-3-030-01237-3_13"},{"key":"1393_CR67","unstructured":"Kristan, M., et\u00a0al. (2014). The visual object tracking VOT2014 challenge results. In European conference on computer vision workshops."},{"key":"1393_CR68","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/nav.3800020109","volume":"2","author":"HW Kuhn","year":"1955","unstructured":"Kuhn, H. W., & Yaw, B. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2, 83\u201397.","journal-title":"Naval Research Logistics Quarterly"},{"key":"1393_CR69","doi-asserted-by":"crossref","unstructured":"Kutschbach, T., Bochinski, E., Eiselein, V., & Sikora, T. (2017). Sequential sensor fusion combining probability hypothesis density and kernelized correlation filters for multi-object tracking in video data. In International conference on advanced video and signal based surveillance.","DOI":"10.1109\/AVSS.2017.8078517"},{"issue":"9","key":"1393_CR70","doi-asserted-by":"publisher","first-page":"4585","DOI":"10.1109\/TIP.2018.2843129","volume":"27","author":"L Lan","year":"2018","unstructured":"Lan, L., Wang, X., Zhang, S., Tao, D., Gao, W., & Huang, T. S. (2018). Interacting tracklets for multi-object tracking. Transactions on Image Processing, 27(9), 4585\u20134597.","journal-title":"Transactions on Image Processing"},{"key":"1393_CR71","doi-asserted-by":"crossref","unstructured":"Le, N., Heili, A., & Odobez, J.-M. (2016). Long-term time-sensitive costs for CRF-based tracking by detection. In European conference on computer vision workshops.","DOI":"10.1007\/978-3-319-48881-3_4"},{"key":"1393_CR72","doi-asserted-by":"crossref","unstructured":"Leal-Taixe, L., Canton-Ferrer, C., & Schindler, K. (2016). Learning by tracking: Siamese CNN for robust target association. In Conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPRW.2016.59"},{"key":"1393_CR73","doi-asserted-by":"crossref","unstructured":"Leal-Taix\u00e9, L., Fenzi, M., Kuznetsova, A., Rosenhahn, B., & Savarese, S. (2014). Learning an image-based motion context for multiple people tracking. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2014.453"},{"key":"1393_CR74","doi-asserted-by":"crossref","unstructured":"Leal-Taix\u00e9, L., Pons-Moll, G., & Rosenhahn, B. (2011). Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker. In International conference on computer vision workshops.","DOI":"10.1109\/ICCVW.2011.6130233"},{"key":"1393_CR75","doi-asserted-by":"publisher","first-page":"8181","DOI":"10.1109\/ACCESS.2018.2889442","volume":"7","author":"S Lee","year":"2019","unstructured":"Lee, S., & Kim, E. (2019). Multiple object tracking via feature pyramid Siamese networks. Access, 7, 8181\u20138194.","journal-title":"Access"},{"key":"1393_CR76","doi-asserted-by":"publisher","first-page":"67316","DOI":"10.1109\/ACCESS.2018.2879535","volume":"6","author":"S-H Lee","year":"2018","unstructured":"Lee, S.-H., Kim, M.-Y., & Bae, S.-H. (2018). Learning discriminative appearance models for online multi-object tracking with appearance discriminability measures. Access, 6, 67316\u201367328.","journal-title":"Access"},{"key":"1393_CR77","doi-asserted-by":"crossref","unstructured":"Levinkov, E., Uhrig, J., Tang, S., Omran, M., Insafutdinov, E., Kirillov, A., Rother, C., Brox, T., Schiele, B., & Andres, B. (2017). Joint graph decomposition and node labeling: Problem, algorithms, applications. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2017.206"},{"key":"1393_CR78","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018). High performance visual tracking with Siamese region proposal network. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2018.00935"},{"key":"1393_CR79","unstructured":"Li, Y., Huang, C., & Nevatia, R. (2009). Learning to associate: Hybrid boosted multi-target tracker for crowded scene. In Conference on computer vision and pattern recognition."},{"key":"1393_CR80","doi-asserted-by":"publisher","first-page":"76489","DOI":"10.1109\/ACCESS.2019.2921975","volume":"7","author":"Q Liu","year":"2019","unstructured":"Liu, Q., Liu, B., Wu, Y., Li, W., & Yu, N. (2019). Real-time online multi-object tracking in compressed domain. Access, 7, 76489\u201376499.","journal-title":"Access"},{"key":"1393_CR81","unstructured":"Long, C., Haizhou, A., Chong, S., Zijie, Z., & Bo, B. (2017). Online multi-object tracking with convolutional neural networks. In International conference on image processing."},{"key":"1393_CR82","unstructured":"Long, C., Haizhou, A., Zijie, Z., & Chong, S. (2018) Real-time multiple people tracking with deeply learned candidate selection and person re-identification. In International conference on multimedia and expo."},{"key":"1393_CR83","doi-asserted-by":"crossref","unstructured":"Loumponias, K., Dimou, A., Vretos, N., & Daras, P. (2018). Adaptive tobit Kalman-based tracking. In International conference on signal-image technology & internet-based systems.","DOI":"10.1109\/SITIS.2018.00021"},{"key":"1393_CR84","doi-asserted-by":"crossref","unstructured":"Ma, C., Yang, C., Yang, F., Zhuang, Y., Zhang, Z., Jia, H., & Xie, X. (2018a). Trajectory factory: Tracklet cleaving and re-connection by deep Siamese bi-GRU for multiple object tracking. In International conference on multimedia and expo.","DOI":"10.1109\/ICME.2018.8486454"},{"key":"1393_CR85","doi-asserted-by":"crossref","unstructured":"Ma, L., Tang, S., Black, M. J., & Van Gool, L. (2018b). Customized multi-person tracker. In Asian conference on computer vision.","DOI":"10.1007\/978-3-030-20890-5_39"},{"issue":"11","key":"1393_CR86","doi-asserted-by":"publisher","first-page":"217","DOI":"10.14569\/IJACSA.2017.081129","volume":"8","author":"H Mahgoub","year":"2017","unstructured":"Mahgoub, H., Mostafa, K., Wassif, K. T., & Farag, I. (2017). Multi-target tracking using hierarchical convolutional features and motion cues. International Journal of Advanced Computer Science & Applications, 8(11), 217\u2013222.","journal-title":"International Journal of Advanced Computer Science & Applications"},{"key":"1393_CR87","doi-asserted-by":"crossref","unstructured":"Maksai, A., & Fua, P. (2019). Eliminating exposure bias and metric mismatch in multiple object tracking. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2019.00477"},{"key":"1393_CR88","doi-asserted-by":"crossref","unstructured":"Manen, S., Timofte, R., Dai, D., & Gool, L. V. (2016). Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure. In Winter conference on applications of computer vision.","DOI":"10.1109\/WACV.2016.7477566"},{"key":"1393_CR89","doi-asserted-by":"crossref","unstructured":"Mathias, M., Benenson, R., Pedersoli, M., & Gool, L. V. (2014). Face detection without bells and whistles. In European conference on computer vision workshops.","DOI":"10.1007\/978-3-319-10593-2_47"},{"key":"1393_CR90","doi-asserted-by":"crossref","unstructured":"McLaughlin, N., Martinez Del Rincon, J., Miller, P. (2015). Enhancing linear programming with motion modeling for multi-target tracking. In Winter conference on applications of computer vision.","DOI":"10.1109\/WACV.2015.17"},{"key":"1393_CR91","doi-asserted-by":"crossref","unstructured":"Milan, A., Leal-Taix\u00e9, L., Schindler, K., & Reid, I. (2015). Joint tracking and segmentation of multiple targets. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2015.7299178"},{"key":"1393_CR92","doi-asserted-by":"crossref","unstructured":"Milan, A., Rezatofighi, S. H., Dick, A., Reid, I., & Schindler, K. (2017). Online multi-target tracking using recurrent neural networks. In Conference on artificial on intelligence.","DOI":"10.1609\/aaai.v31i1.11194"},{"issue":"1","key":"1393_CR93","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/TPAMI.2013.103","volume":"36","author":"A Milan","year":"2014","unstructured":"Milan, A., Roth, S., & Schindler, K. (2014). Continuous energy minimization for multitarget tracking. Transactions on Pattern Analysis and Machine Intelligence, 36(1), 58\u201372.","journal-title":"Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1393_CR94","doi-asserted-by":"crossref","unstructured":"Milan, A., Schindler, K., & Roth, S. (2013). Challenges of ground truth evaluation of multi-target tracking. In Conference on computer vision and pattern recognition workshops.","DOI":"10.1109\/CVPRW.2013.111"},{"issue":"10","key":"1393_CR95","doi-asserted-by":"publisher","first-page":"2054","DOI":"10.1109\/TPAMI.2015.2505309","volume":"38","author":"A Milan","year":"2016","unstructured":"Milan, A., Schindler, K., & Roth, S. (2016). Multi-target tracking by discrete-continuous energy minimization. Transactions on Pattern Analysis and Machine Intelligence, 38(10), 2054\u20132068.","journal-title":"Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1393_CR96","doi-asserted-by":"crossref","unstructured":"Nguyen Thi Lan Anh, F. N., Khan, Furqan, & Bremond, F. (2017). Multi-object tracking using multi-channel part appearance representation. In International conference on advanced video and signal based surveillance.","DOI":"10.1109\/AVSS.2017.8078552"},{"key":"1393_CR97","doi-asserted-by":"crossref","unstructured":"Pedersen, M., Haurum, J. B., Bengtson, S. H., & Moeslund, T. B. (June 2020). 3D-ZEF: A 3D zebrafish tracking benchmark dataset. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR42600.2020.00250"},{"key":"1393_CR98","doi-asserted-by":"crossref","unstructured":"Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011). Globally-optimal greedy algorithms for tracking a variable number of objects. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2011.5995604"},{"issue":"6","key":"1393_CR99","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1109\/TAC.1979.1102177","volume":"24","author":"DB Reid","year":"1979","unstructured":"Reid, D. B. (1979). An algorithm for tracking multiple targets. Transactions on Automatic Control, 24(6), 843\u2013854.","journal-title":"Transactions on Automatic Control"},{"key":"1393_CR100","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems."},{"key":"1393_CR101","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Milan, A., Zhang, Z., Shi, Q., Dick, A., & Reid, I. (2015). Joint probabilistic data association revisited. In International conference on computer vision.","DOI":"10.1109\/ICCV.2015.349"},{"key":"1393_CR102","doi-asserted-by":"crossref","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In European conference on computer vision.","DOI":"10.1007\/978-3-319-48881-3_2"},{"issue":"3","key":"1393_CR103","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211\u2013252.","journal-title":"International Journal of Computer Vision"},{"key":"1393_CR104","doi-asserted-by":"crossref","unstructured":"Sadeghian, A., Alahi, A., Savarese, S. (2017). Tracking the untrackable: Learning to track multiple cues with long-term dependencies. In International conference on computer vision.","DOI":"10.1109\/ICCV.2017.41"},{"key":"1393_CR105","doi-asserted-by":"crossref","unstructured":"Sanchez-Matilla, R., Cavallaro, A. (2019). A predictor of moving objects for first-person vision. In International conference on image processing.","DOI":"10.1109\/ICIP.2019.8803140"},{"key":"1393_CR106","doi-asserted-by":"crossref","unstructured":"Sanchez-Matilla, R., Poiesi, F., & Cavallaro, A. (2016). Online multi-target tracking with strong and weak detections. In European conference on computer vision workshops.","DOI":"10.1007\/978-3-319-48881-3_7"},{"issue":"1","key":"1393_CR107","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/A:1014573219977","volume":"47","author":"D Scharstein","year":"2002","unstructured":"Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1), 7\u201342.","journal-title":"International Journal of Computer Vision"},{"issue":"8","key":"1393_CR108","doi-asserted-by":"publisher","first-page":"3447","DOI":"10.1109\/TSP.2008.920469","volume":"56","author":"D Schuhmacher","year":"2008","unstructured":"Schuhmacher, D., Vo, B.-T., & Vo, B.-N. (2008). A consistent metric for performance evaluation of multi-object filters. Transactions on Signal Processing, 56(8), 3447\u20133457.","journal-title":"Transactions on Signal Processing"},{"key":"1393_CR109","doi-asserted-by":"crossref","unstructured":"Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., & Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2006.19"},{"issue":"12","key":"1393_CR110","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.1109\/TCSVT.2018.2881123","volume":"29","author":"H Sheng","year":"2018","unstructured":"Sheng, H., Chen, J., Zhang, Y., Ke, W., Xiong, Z., & Yu, J. (2018a). Iterative multiple hypothesis tracking with tracklet-level association. Transactions on Circuits and Systems for Video Technology, 29(12), 3660\u20133672.","journal-title":"Transactions on Circuits and Systems for Video Technology"},{"key":"1393_CR111","unstructured":"Sheng, H., Hao, L., Chen, J., et\u00a0al. (2017). Robust local effective matching model for multi-target tracking. In Advances in multimedia information processing (Vol. 127, No. 8)."},{"key":"1393_CR112","doi-asserted-by":"publisher","first-page":"2107","DOI":"10.1109\/ACCESS.2018.2881019","volume":"7","author":"H Sheng","year":"2018","unstructured":"Sheng, H., Zhang, X., Zhang, Y., Wu, Y., & Chen, J. (2018b). Enhanced association with supervoxels in multiple hypothesis tracking. Access, 7, 2107\u20132117.","journal-title":"Access"},{"issue":"11","key":"1393_CR113","doi-asserted-by":"publisher","first-page":"3269","DOI":"10.1109\/TCSVT.2018.2882192","volume":"29","author":"H Sheng","year":"2018","unstructured":"Sheng, H., Zhang, Y., Chen, J., Xiong, Z., & Zhang, J. (2018c). Heterogeneous association graph fusion for target association in multiple object tracking. Transactions on Circuits and Systems for Video Technology, 29(11), 3269\u20133280.","journal-title":"Transactions on Circuits and Systems for Video Technology"},{"key":"1393_CR114","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1007\/s11263-018-01147-z","volume":"127","author":"X Shi","year":"2018","unstructured":"Shi, X., Ling, H., Pang, Y. Y., Hu, W., Chu, P., & Xing, J. (2018). Rank-1 tensor approximation for high-order association in multi-target tracking. International Journal of Computer Vision, 127, 1063\u20131083.","journal-title":"International Journal of Computer Vision"},{"key":"1393_CR115","doi-asserted-by":"crossref","unstructured":"Smith, K., Gatica-Perez, D., Odobez, J.-M., & Ba, S. (2005). Evaluating multi-object tracking. In Workshop on empirical evaluation methods in computer vision.","DOI":"10.1109\/CVPR.2005.453"},{"key":"1393_CR116","doi-asserted-by":"crossref","unstructured":"Son, J., Baek, M., Cho, M., & Han, B. (2017). Multi-object tracking with quadruplet convolutional neural networks. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2017.403"},{"key":"1393_CR117","doi-asserted-by":"crossref","unstructured":"Song, Y., & Jeon, M. (2016). Online multiple object tracking with the hierarchically adopted GM-PHD filter using motion and appearance. In International conference on consumer electronics.","DOI":"10.1109\/ICCE-Asia.2016.7804800"},{"key":"1393_CR118","unstructured":"Song, Y., Yoon, Y., Yoon, K., & Jeon, M. (2018). Online and real-time tracking with the GMPHD filter using group management and relative motion analysis. In International conference on advanced video and signal based surveillance."},{"key":"1393_CR119","doi-asserted-by":"publisher","first-page":"165103","DOI":"10.1109\/ACCESS.2019.2953276","volume":"7","author":"Y Song","year":"2019","unstructured":"Song, Y., Yoon, K., Yoon, Y., Yow, K., & Jeon, M. (2019). Online multi-object tracking with GMPHD filter and occlusion group management. Access, 7, 165103\u2013165121.","journal-title":"Access"},{"key":"1393_CR120","unstructured":"Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J. S., Mostefa, D., & Soundararajan, P. (2006). The clear 2006 evaluation. In Multimodal technologies for perception of humans."},{"key":"1393_CR121","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., & Caine, B., et\u00a0al. (2020). Scalability in perception for autonomous driving: Waymo open dataset. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"1393_CR122","doi-asserted-by":"crossref","unstructured":"Tang, S., Andres, B., Andriluka, M., & Schiele, B. (2015). Subgraph decomposition for multi-target tracking. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2015.7299138"},{"key":"1393_CR123","doi-asserted-by":"crossref","unstructured":"Tang, S., Andres, B., Andriluka, M., & Schiele, B. (2016). Multi-person tracking by multicuts and deep matching. In European conference on computer vision workshops.","DOI":"10.1007\/978-3-319-48881-3_8"},{"key":"1393_CR124","doi-asserted-by":"crossref","unstructured":"Tang, S., Andriluka, M., Andres, B., & Schiele, B. (2017). Multiple people tracking with lifted multicut and person re-identification. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2017.394"},{"key":"1393_CR125","doi-asserted-by":"crossref","unstructured":"Tao, Y., Chen, J., Fang, Y., Masaki, I., & Horn, B. K. (2018). Adaptive spatio-temporal model based multiple object tracking in video sequences considering a moving camera. In International conference on universal village.","DOI":"10.1109\/UV.2018.8642156"},{"key":"1393_CR126","unstructured":"Taskar, B., Guestrin, C., & Koller, D. (2003). Max-margin Markov networks. In Advances in neural information processing systems."},{"key":"1393_CR127","volume-title":"Probabilistic robotics (intelligent robotics and autonomous agents)","author":"S Thrun","year":"2005","unstructured":"Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics (intelligent robotics and autonomous agents). Cambridge: The MIT Press."},{"issue":"1","key":"1393_CR128","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/TITS.2019.2892413","volume":"21","author":"W Tian","year":"2019","unstructured":"Tian, W., Lauer, M., & Chen, L. (2019). Online multi-object tracking using joint domain information in traffic scenarios. Transactions on Intelligent Transportation Systems, 21(1), 374\u2013384.","journal-title":"Transactions on Intelligent Transportation Systems"},{"key":"1393_CR129","doi-asserted-by":"crossref","unstructured":"Torralba, A., & Efros, A. A. (2011). Unbiased look at dataset bias. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"1393_CR130","doi-asserted-by":"crossref","unstructured":"Wang, B., Wang, L., Shuai, B., Zuo, Z., Liu, T., et\u00a0al. (2016). Joint learning of convolutional neural networks and temporally constrained metrics for tracklet association. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPRW.2016.55"},{"key":"1393_CR131","doi-asserted-by":"crossref","unstructured":"Wang, G., Wang, Y., Zhang, H., Gu, R., & Hwang, J.-N. (2019). Exploit the connectivity: Multi-object tracking with trackletnet. In International conference on multimedia.","DOI":"10.1145\/3343031.3350853"},{"issue":"3","key":"1393_CR132","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1007\/s11263-016-0960-z","volume":"122","author":"S Wang","year":"2016","unstructured":"Wang, S., & Fowlkes, C. (2016). Learning optimal parameters for multi-target tracking with contextual interactions. International Journal of Computer Vision, 122(3), 484\u2013501.","journal-title":"International Journal of Computer Vision"},{"key":"1393_CR133","doi-asserted-by":"publisher","first-page":"102907","DOI":"10.1016\/j.cviu.2020.102907","volume":"193","author":"L Wen","year":"2020","unstructured":"Wen, L., Du, D., Cai, Z., Lei, Z., Chang, M., Qi, H., et al. (2020). UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking. Computer Vision and Image Understanding, 193, 102907.","journal-title":"Computer Vision and Image Understanding"},{"key":"1393_CR134","doi-asserted-by":"crossref","unstructured":"Wen, L., Li, W., Yan, J., Lei, Z., Yi, D., & Li, S. Z. (2014). Multiple target tracking based on undirected hierarchical relation hypergraph. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2014.167"},{"key":"1393_CR135","doi-asserted-by":"crossref","unstructured":"Wojke, N., & Paulus, D. (2016). Global data association for the probability hypothesis density filter using network flows. International conference on robotics and automation.","DOI":"10.1109\/ICRA.2016.7487180"},{"key":"1393_CR136","unstructured":"Wu, B., & Nevatia, R. (2006). Tracking of multiple, partially occluded humans based on static body part detection. In Conference on computer vision and pattern recognition."},{"key":"1393_CR137","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.patcog.2019.04.018","volume":"94","author":"H Wu","year":"2019","unstructured":"Wu, H., Hu, Y., Wang, K., Li, H., Nie, L., & Cheng, H. (2019). Instance-aware representation learning and association for online multi-person tracking. Pattern Recognition, 94, 25\u201334.","journal-title":"Pattern Recognition"},{"key":"1393_CR138","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2975842","author":"J Xiang","year":"2020","unstructured":"Xiang, J., Xu, G., Ma, C., & Hou, J. (2020). End-to-end learning deep CRF models for multi-object tracking. Transactions on Circuits and Systems for Video Technology,. https:\/\/doi.org\/10.1109\/TCSVT.2020.2975842.","journal-title":"Transactions on Circuits and Systems for Video Technology"},{"key":"1393_CR139","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Alahi, A., & Savarese, S. (2015). Learning to track: Online multi-object tracking by decision making. In International conference on computer vision.","DOI":"10.1109\/ICCV.2015.534"},{"key":"1393_CR140","doi-asserted-by":"crossref","unstructured":"Xu, J., Cao, Y., Zhang, Z., & Hu, H. (2019). Spatial-temporal relation networks for multi-object tracking. In International conference on computer vision.","DOI":"10.1109\/ICCV.2019.00409"},{"key":"1393_CR141","doi-asserted-by":"crossref","unstructured":"Xu, Y., Osep, A., Ban, Y., Horaud, R., Leal-Taixe, L., & Alameda-Pineda, X. (2020). How to train your deep multi-object tracker. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR42600.2020.00682"},{"key":"1393_CR142","doi-asserted-by":"crossref","unstructured":"Yang, F., Choi, W., & Lin, Y. (2016). Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2016.234"},{"key":"1393_CR143","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2016.05.003","author":"M Yang","year":"2016","unstructured":"Yang, M., & Jia, Y. (2016). Temporal dynamic appearance modeling for online multi-person tracking. Computer Vision and Image Understanding,. https:\/\/doi.org\/10.1016\/j.cviu.2016.05.003.","journal-title":"Computer Vision and Image Understanding"},{"key":"1393_CR144","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2745103","author":"M Yang","year":"2017","unstructured":"Yang, M., Wu, Y., & Jia, Y. (2017). A hybrid data association framework for robust online multi-object tracking. Transactions on Image Processing,. https:\/\/doi.org\/10.1109\/TIP.2017.2745103.","journal-title":"Transactions on Image Processing"},{"key":"1393_CR145","doi-asserted-by":"crossref","unstructured":"Yoon, J., Yang, H., Lim, J., & Yoon, K. (2015). Bayesian multi-object tracking using motion context from multiple objects. In Winter conference on applications of computer vision.","DOI":"10.1109\/WACV.2015.12"},{"key":"1393_CR146","doi-asserted-by":"crossref","unstructured":"Yoon, J. H., Lee, C. R., Yang, M. H., & Yoon, K. J. (2016). Online multi-object tracking via structural constraint event aggregation. In International conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2016.155"},{"key":"1393_CR147","doi-asserted-by":"publisher","first-page":"38060","DOI":"10.1109\/ACCESS.2020.2975912","volume":"8","author":"K Yoon","year":"2020","unstructured":"Yoon, K., Gwak, J., Song, Y., Yoon, Y., & Jeon, M. (2020). OneShotDa: Online multi-object tracker with one-shot-learning-based data association. Access, 8, 38060\u201338072.","journal-title":"Access"},{"key":"1393_CR148","doi-asserted-by":"publisher","first-page":"559","DOI":"10.3390\/s19030559","volume":"19","author":"K Yoon","year":"2019","unstructured":"Yoon, K., Kim, D. Y., Yoon, Y.-C., & Jeon, M. (2019a). Data association for multi-object tracking via deep neural networks. Sensors, 19, 559.","journal-title":"Sensors"},{"key":"1393_CR149","doi-asserted-by":"crossref","unstructured":"Yoon, Y., Boragule, A., Song, Y., Yoon, K., & Jeon, M. (2018a). Online multi-object tracking with historical appearance matching and scene adaptive detection filtering. In International conference on advanced video and signal based surveillance.","DOI":"10.1109\/AVSS.2018.8639078"},{"key":"1393_CR150","unstructured":"Yoon, Y., Kim, D. Y., Yoon, K., Song, Y., & Jeon, M. (2019b). Online multiple pedestrian tracking using deep temporal appearance matching association. arXiv preprint arXiv:1907.00831."},{"key":"1393_CR151","doi-asserted-by":"crossref","unstructured":"Yoon, Y.-C., Song, Y.-M., Yoon, K., & Jeon, M. (2018). Online multi-object tracking using selective deep appearance matching. In International conference on consumer electronics Asia.","DOI":"10.1109\/ICCE-ASIA.2018.8552105"},{"key":"1393_CR152","unstructured":"Zamir, A. R., Dehghan, A., & Shah, M. (2012). GMCP-Tracker: Global multi-object tracking using generalized minimum clique graphs. In European conference on computer vision."},{"key":"1393_CR153","doi-asserted-by":"crossref","unstructured":"Zhang, L., Li, Y., & Nevatia, R. (2008). Global data association for multi-object tracking using network flows. In Conference on computer vision and pattern recognition.","DOI":"10.1109\/CVPR.2008.4587584"},{"key":"1393_CR154","doi-asserted-by":"publisher","first-page":"6694","DOI":"10.1109\/TIP.2020.2993073","volume":"29","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Sheng, H., Wu, Y., Wang, S., Lyu, W., Ke, W., et al. (2020). Long-term tracking with deep tracklet association. Transactions on Image Processing, 29, 6694\u20136706.","journal-title":"Transactions on Image Processing"},{"key":"1393_CR155","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2018.2825679","author":"H Zhou","year":"2018","unstructured":"Zhou, H., Ouyang, W., Cheng, J., Wang, X., & Li, H. (2018). Deep continuous conditional random fields with asymmetric inter-object constraints for online multi-object tracking. Transactions on Circuits and Systems for Video Technology,. https:\/\/doi.org\/10.1109\/TCSVT.2018.2825679.","journal-title":"Transactions on Circuits and Systems for Video Technology"},{"key":"1393_CR156","unstructured":"Zhou, X., Jiang, P., Wei, Z., Dong, H., & Wang, F. (2018b). Online multi-object tracking with structural invariance constraint. In British machine vision conference."},{"key":"1393_CR157","doi-asserted-by":"crossref","unstructured":"Zhu, J., Yang, H., Liu, N., Kim, M., Zhang, W., & Yang, M.-H. (2018). Online multi-object tracking with dual matching attention networks. In European conference on computer vision workshops.","DOI":"10.1007\/978-3-030-01228-1_23"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01393-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11263-020-01393-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-020-01393-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T23:55:27Z","timestamp":1724111727000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11263-020-01393-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,23]]},"references-count":157,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["1393"],"URL":"https:\/\/doi.org\/10.1007\/s11263-020-01393-0","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,23]]},"assertion":[{"value":"1 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}