{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:11:59Z","timestamp":1760242319913,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T00:00:00Z","timestamp":1492473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005073","name":"Agency for Defense Development","doi-asserted-by":"publisher","award":["UD150016ID"],"award-info":[{"award-number":["UD150016ID"]}],"id":[{"id":"10.13039\/501100005073","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT) and KITTIbenchmarks.<\/jats:p>","DOI":"10.3390\/s17040883","type":"journal-article","created":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T11:22:04Z","timestamp":1492514524000},"page":"883","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3972-5119","authenticated-orcid":false,"given":"Sang-Il","family":"Oh","sequence":"first","affiliation":[{"name":"Department of Media Engineering, Catholic University of Korea, 43-1, Yeoggok 2-dong, Wonmmi-gu, Bucheon-si, Gyeonggi-do 14662, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7064-478X","authenticated-orcid":false,"given":"Hang-Bong","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Media Engineering, Catholic University of Korea, 43-1, Yeoggok 2-dong, Wonmmi-gu, Bucheon-si, Gyeonggi-do 14662, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,18]]},"reference":[{"key":"ref_1","unstructured":"Lucas, B.D., and Kanade, T. (1981, January 24\u201328). An iterative image registration technique with an application to stereo vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, BC, Canada."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s11263-006-7067-x","article-title":"Robust tracking using foreground-background texture discrimination","volume":"69","author":"Nguyen","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pan, J., and Hu, B. (2007, January 17\u201322). Robust occlusion handling in object tracking. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383453"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1109\/TIP.2014.2300823","article-title":"Robust superpixel tracking","volume":"23","author":"Yang","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","unstructured":"Chen, X., Kundu, K., Zhu, Y., Berneshawi, A.G., Ma, H., Fidler, S., and Urtasun, R. (2015). 3D object proposals for accurate object class detection. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lai, K., Bo, L., Ren, X., and Fox, D. (2011, January 9\u201313). A large-scale hierarchical multi-view RGB-d object dataset. Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980382"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Premebida, C., Carreira, J., Batista, J., and Nunes, U. (2014, January 14\u201318). Pedestrian detection combining RGB and dense lidar data. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943141"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Spinello, L., and Arras, K.O. (2011, January 25\u201330). People detection in RGB-D data. Proceedings of the 2011 IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA.","DOI":"10.1109\/IROS.2011.6095074"},{"key":"ref_9","unstructured":"Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H. (2016). Generative adversarial text to image synthesis. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7258","DOI":"10.1109\/JSEN.2016.2598600","article-title":"Fast Occupancy Grid Filtering Using Grid Cell Clusters From LIDAR and Stereo Vision Sensor Data","volume":"16","author":"Oh","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tao, R., Gavves, E., and Smeulders, A.W. (2016). Siamese Instance Search for Tracking. arXiv.","DOI":"10.1109\/CVPR.2016.158"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Held, D., Thrun, S., and Savarese, S. (2016). Learning to Track at 100 FPS with Deep Regression Networks. arXiv.","DOI":"10.1007\/978-3-319-46448-0_45"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kong, T., Yao, A., Chen, Y., and Sun, F. (2016). HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.98"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1002\/sdtp.10703","article-title":"35-2: Invited Paper: RGB-D Image Understanding using Supervision Transfer","volume":"Volume 47","author":"Gupta","year":"2016","journal-title":"SID Symposium Digest of Technical Papers"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Shahbaz Khan, F., and Felsberg, M. (2015, January 7\u201313). Learning spatially regularized correlation filters for visual tracking. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.490"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nam, H., and Han, B. (2015). Learning multi-domain convolutional neural networks for visual tracking. arXiv.","DOI":"10.1109\/CVPR.2016.465"},{"key":"ref_17","unstructured":"Hong, S., You, T., Kwak, S., and Han, B. (2015). Online tracking by learning discriminative saliency map with convolutional neural network. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2964","DOI":"10.1016\/j.patcog.2015.02.012","article-title":"Self-taught learning of a deep invariant representation for visual tracking via temporal slowness principle","volume":"48","author":"Kuen","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TIP.2015.2510583","article-title":"Deeptrack: Learning discriminative feature representations online for robust visual tracking","volume":"25","author":"Li","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, L., Ouyang, W., Wang, X., and Lu, H. (2015, January 7\u201313). Visual tracking with fully convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.357"},{"key":"ref_21","unstructured":"Xu, M., Orwell, J., and Jones, G. (2004, January 24\u201327). Tracking football players with multiple cameras. Proceedings of the 2004 International Conference on Image Processing, Singapore."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cheng, X., Honda, M., Ikoma, N., and Ikenaga, T. (2016, January 20\u201325). Anti-occlusion observation model and automatic recovery for multi-view ball tracking in sports analysis. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7471927"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., and Fitzgibbon, A. (2011, January 26\u201329). KinectFusion: Real-time dense surface mapping and tracking. Proceedings of the 2011 10th IEEE international Symposium on Mixed and Augmented Reality (ISMAR), Basel, Switzerland.","DOI":"10.1109\/ISMAR.2011.6092378"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cho, H., Seo, Y.W., Kumar, B.V., and Rajkumar, R.R. (June, January 31). A multi-sensor fusion system for moving object detection and tracking in urban driving environments. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907100"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Allodi, M., Broggi, A., Giaquinto, D., Patander, M., and Prioletti, A. (2016, January 19\u201322). Machine learning in tracking associations with stereo vision and lidar observations for an autonomous vehicle. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gotenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535456"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1002\/rob.20312","article-title":"LIDAR and vision-based pedestrian detection system","volume":"26","author":"Premebida","year":"2009","journal-title":"J. Field Robot."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1002\/rob.21430","article-title":"Moving object detection with laser scanners","volume":"30","author":"Mertz","year":"2013","journal-title":"J. Field Robot."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1016\/S0957-4158(03)00047-3","article-title":"Perception for collision avoidance and autonomous driving","volume":"13","author":"Gowdy","year":"2003","journal-title":"Mechatronics"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D. (2012, January 7\u201312). A benchmark for the evaluation of RGB-D SLAM systems. Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Teutsch, M., Muller, T., Huber, M., and Beyerer, J. (2014, January 23\u201328). Low resolution person detection with a moving thermal infrared camera by hot spot classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.40"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"K\u00fcmmerle, J., Hinzmann, T., Vempati, A.S., and Siegwart, R. (2016). Real-Time Detection and Tracking of Multiple Humans from High Bird\u2019s-Eye Views in the Visual and Infrared Spectrum. International Symposium on Visual Computing, Springer.","DOI":"10.1007\/978-3-319-50835-1_49"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez, A., V\u00e1zquez, D., L\u00f3opez, A.M., and Amores, J. (2016). On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts. IEEE Trans. Cybern.","DOI":"10.1109\/TCYB.2016.2593940"},{"key":"ref_33","unstructured":"Wang, R., Bach, J., Macfarlane, J., and Ferrie, F.P. (2012, January 9\u201311). A new upsampling method for mobile LiDAR data. Proceedings of the 2012 IEEE Workshop on Applications of Computer Vision (WACV), Breckenridge, CO, USA."},{"key":"ref_34","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, F., Choi, W., and Lin, Y. (2016, January 27\u201330). Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.234"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., Yang, M., Zhu, S., and Lin, Y. (2013, January 1\u20138). Regionlets for generic object detection. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.10"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/34.55104","article-title":"The combination of evidence in the transferable belief model","volume":"12","author":"Smets","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"Yager, R., Fedrizzi, M., and Kacprzyk, J. (1994). Advances in the Dempster-Shafer Theory of Evidence, John Wiley & Sons."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Stiller, C., and Urtasun, R. (2013). Vision meets robotics: The KITTI dataset. Int. J. Robot. Res.","DOI":"10.1177\/0278364913491297"},{"key":"ref_43","unstructured":"Leal-Taix\u00e9, L., Milan, A., Reid, I., Roth, S., and Schindler, K. (2015). MOTChallenge 2015: Towards a benchmark for multi-target tracking. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2008\/246309","article-title":"Evaluating multiple object tracking performance: The CLEAR MOT metrics","volume":"2008","author":"Bernardin","year":"2008","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, C., and Nevatia, R. (2009, January 20\u201325). Learning to associate: Hybridboosted multi-target tracker for crowded scene. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206735"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.imavis.2015.01.003","article-title":"Robust stereo matching using adaptive random walk with restart algorithm","volume":"37","author":"Lee","year":"2015","journal-title":"Image Vis. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_48","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_49","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I., Roth, S., and Schindler, K. (2016). MOT16: A Benchmark for Multi-Object Tracking. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1012","DOI":"10.1109\/TPAMI.2013.185","article-title":"3D traffic scene understanding from movable platforms","volume":"36","author":"Geiger","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Le, N., Heili, A., and Odobez, J.M. (2016). Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-48881-3_4"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ban, Y., Ba, S., Alameda-Pineda, X., and Horaud, R. (2016). Tracking Multiple Persons Based on a Variational Bayesian Model. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-48881-3_5"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sanchez-Matilla, R., Poiesi, F., and Cavallaro, A. (2016). Online multi-target tracking with strong and weak detections. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-48881-3_7"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Fagot-Bouquet, L., Audigier, R., Dhome, Y., and Lerasle, F. (2016). Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46484-8_47"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kim, C., Li, F., Ciptadi, A., and Rehg, J.M. (2015, January 7\u201313). Multiple hypothesis tracking revisited. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.533"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kieritz, H., Becker, S., H\u00fcbner, W., and Arens, M. (2016, January 23\u201326). Online multi-person tracking using Integral Channel Features. Proceedings of the 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Colorado Springs, CO, USA.","DOI":"10.1109\/AVSS.2016.7738059"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tang, S., Andres, B., Andriluka, M., and Schiele, B. (2015, January 7\u201312). Subgraph decomposition for multi-target tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299138"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Choi, W. (2015, January 7\u201313). Near-online multi-target tracking with aggregated local flow descriptor. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.347"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_60","unstructured":"Ju, H.Y., Lee, C.R., Yang, M.H., and Yoon, K.J. (2016, January 27\u201330). Online multi-object tracking via structural constraint event aggregation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TPAMI.2013.103","article-title":"Continuous Energy Minimization for Multitarget Tracking","volume":"36","author":"Milan","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Andriyenko, A., Schindler, K., and Roth, S. (2012, January 16\u201321). Discrete-Continuous Optimization for Multi-Target Tracking. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247893"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lenz, P., Geiger, A., and Urtasun, R. (2015, January 7\u201313). FollowMe: Efficient online min-cost flow tracking with bounded memory and computation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.496"},{"key":"ref_64","unstructured":"Geiger, A. (2013). Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms, KIT Scientific Publishing."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Pirsiavash, H., Ramanan, D., and Fowlkes, C.C. (2011, January 20\u201325). Globally-optimal greedy algorithms for tracking a variable number of objects. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995604"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lee, B., Erdenee, E., Jin, S., Nam, M.Y., Jung, Y.G., and Rhee, P.K. (2016). Multi-class Multi-object Tracking Using Changing Point Detection. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-48881-3_6"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Yoon, J.H., Yang, M.H., Lim, J., and Yoon, K.J. (2015, January 5\u20139). Bayesian multi-object tracking using motion context from multiple objects. Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.12"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/883\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:32:51Z","timestamp":1760207571000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/4\/883"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,4,18]]},"references-count":67,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2017,4]]}},"alternative-id":["s17040883"],"URL":"https:\/\/doi.org\/10.3390\/s17040883","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2017,4,18]]}}}