{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:04:39Z","timestamp":1760241879669,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,11]],"date-time":"2018-10-11T00:00:00Z","timestamp":1539216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tracking multiple pedestrians across multi-camera scenarios is an important part of intelligent video surveillance and has great potential application for public security, which has been an attractive topic in the literature in recent years. In most previous methods, artificial features such as color histograms, HOG descriptors and Haar-like feature were adopted to associate objects among different cameras. But there are still many challenges caused by low resolution, variation of illumination, complex background and posture change. In this paper, a feature extraction network named NCA-Net is designed to improve the performance of multiple objects tracking across multiple cameras by avoiding the problem of insufficient robustness caused by hand-crafted features. The network combines features learning and metric learning via a Convolutional Neural Network (CNN) model and the loss function similar to neighborhood components analysis (NCA). The loss function is adapted from the probability loss of NCA aiming at object tracking. The experiments conducted on the NLPR_MCT dataset show that we obtain satisfactory results even with a simple matching operation. In addition, we embed the proposed NCA-Net with two existing tracking systems. The experimental results on the corresponding datasets demonstrate that the extracted features using NCA-net can effectively make improvement on the tracking performance.<\/jats:p>","DOI":"10.3390\/s18103400","type":"journal-article","created":{"date-parts":[[2018,10,12]],"date-time":"2018-10-12T02:58:04Z","timestamp":1539313084000},"page":"3400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["NCA-Net for Tracking Multiple Objects across Multiple Cameras"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0963-5339","authenticated-orcid":false,"given":"Yihua","family":"Tan","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science &amp; Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Luoyu Road 1037, Hongshan District, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Tai","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science &amp; Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Luoyu Road 1037, Hongshan District, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengzhou","family":"Xiong","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science &amp; Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Luoyu Road 1037, Hongshan District, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Song, H., Srinivasan, R., Sookoor, T., and Jeschke, S. (2017). Smart Cities: Foundations, Principles and Applications, Wiley.","DOI":"10.1002\/9781119226444"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, J., Xu, R., Lv, Z., and Song, H. (2016). Analysis of camera arrays applicable to the internet of things. Sensors, 16.","DOI":"10.3390\/s16030421"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.future.2017.11.015","article-title":"A distributed image-retrieval method in multi-camera system of smart city based on cloud computing","volume":"81","author":"Yang","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_4","unstructured":"Gary, D., Brennan, S., and Tao, H. (2007, January 14). Evaluating appearance models for recognition, reacquisition, and tracking. Proceedings of the IEEE International Workshop on Performance Evaluation for Tracking and Surveillance, Rio de Janeiro, Brazil."},{"key":"ref_5","first-page":"267","article-title":"A multi-camera video dataset for research on high-definition surveillance","volume":"1","author":"Athira","year":"2014","journal-title":"Int. J. Mach. Intell. Sens. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TPAMI.2007.1174","article-title":"Multicamera People Tracking with a Probabilistic Occupancy Map","volume":"30","author":"Fleuret","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1109\/TPAMI.2011.21","article-title":"Multiple Object Tracking Using K-Shortest Paths Optimization","volume":"33","author":"Berclaz","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1614","DOI":"10.1109\/TPAMI.2013.210","article-title":"Multi-Commodity Network Flow for Tracking Multiple People","volume":"36","author":"Shitrit","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1109\/TPAMI.2015.2513406","article-title":"Tracking Interacting Objects Using Intertwined Flows","volume":"38","author":"Wang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","unstructured":"Milan, A., Leal-Taixe, L., Reid, I., Roth, S., and Schindler, K. (arXiv, 2016). Mot16: A benchmark for multi-object tracking, arXiv."},{"key":"ref_11","first-page":"2367","article-title":"An equalized global graph model-based approach for multicamera object tracking","volume":"27","author":"Chen","year":"2017","journal-title":"IEEE Trans. Circuits Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ristani, E., Solera, F., Zou, R.S., Cucchiara, R., and Tomasi, C. (2016, January 8\u201316). Performance measures and a data set for multi-target, multi-camera tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bose, B., Wang, X., and Grimson, E. (2007, January 18\u201323). Multi-class object tracking algorithm that handles fragmentation and grouping. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383175"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Song, B., Jeng, Ti., Staudt, E., and Roy-Chowdhury, A.K. (2010, January 5\u201311). A stochastic graph evolution framework for robust multi-target tracking. Proceedings of the European Conference on Computer Vision, Heraklion, Greece.","DOI":"10.1007\/978-3-642-15549-9_44"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bae, S., and Yoon, K. (2014, January 23\u201328). Robust online multi-object tracking based on tracklet confidence and online discriminative appearance learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.159"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Alahi, A., and Savarese, S. (2015, January 7\u201313). Learning to track: Online multi-object tracking by decision making. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.534"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2420","DOI":"10.1109\/TPAMI.2012.42","article-title":"Single and multiple object tracking using log-Euclidean Riemannian subspace and block-division appearance model","volume":"34","author":"Hu","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Maaten, L.V.D. (2013, January 23\u201328). Structure preserving object tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.240"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Presti, L.L., and Sclaroff, S. (2012, January 18\u201321). Online multi-person tracking by tracker hierarchy. Proceedings of the 9th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Beijing, China.","DOI":"10.1109\/AVSS.2012.51"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yoon, J.H., Yang, M., Lim, J., and Yoon, K. (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"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.1049\/el.2015.1013","article-title":"Quantum particle filter: A multiple mode method for low delay abrupt pedestrian motion tracking","volume":"51","author":"Khalili","year":"2015","journal-title":"Electron. Lett."},{"key":"ref_22","unstructured":"Sugimura, D., Kitani, K.M., Okabe, T., Sato, Y., and Sugimoto, A. (October, January 27). Using individuality to track individuals: Clustering individual trajectories in crowds using local appearance and frequency trait. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mitzel, D., and Leibe, B. (2011, January 6\u201313). Real-time multi-person tracking with detector assisted structure propagation. Proceedings of the IEEE International Conference on Computer Vision Workshops, Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130357"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mitzel, D., Horbert, E., Ess, A., and Leibe, B. (2010, January 5\u201311). Multi-person tracking with sparse detection and continuous segmentation. Proceedings of the Computer Vision 11th European Conference on Computer Vision, Heraklion, Greece.","DOI":"10.1007\/978-3-642-15549-9_29"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Okuma, K., Taleghani, A., Freitas, D.N., and Lowe, D. (2004, January 11\u201314). A boosted particle filter: Multitarget detection and tracking. Proceedings of the European Conference on Computer Vision, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24670-1_3"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2013.08.031","article-title":"Combining patch matching and detection for robust pedestrian tracking in monocular calibrated cameras","volume":"39","author":"Jung","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ma, Y., Chen, X., and Chen, G. (2011, January 21\u201323). Pedestrian detection and tracking using HOG and oriented-LBP features. Proceedings of the 8th IFIP International Conference on Network and Parallel Computing, Changsha, China.","DOI":"10.1007\/978-3-642-24403-2_15"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tsuduki, Y., and Fujiyoshi, H. (2009, January 13\u201316). A method for visualizing pedestrian traffic flow using sift feature point tracking. Proceedings of the Third Pacific Rim Symposium on Advances in Image and Video Technology, Tokyo, Japan.","DOI":"10.1007\/978-3-540-92957-4_3"},{"key":"ref_29","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"38669","DOI":"10.1109\/ACCESS.2018.2854922","article-title":"3D panoramic virtual reality video quality assessment based on 3D convolutional neural networks","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Rodriguez, M., Sivic, J., Laptev, I., and Audibert, J. (2011, January 6\u201313). Data-driven crowd analysis in videos. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126374"},{"key":"ref_35","unstructured":"Breitenstein, M.D., Reichlin, F., Leibe, B., Koller-Meier, E., and Van Gool, L. (October, January 27). Robust tracking-by-detection using a detector confidence particle filter. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Le, N., Heili, A., and Odobez, J. (2016, January 8\u201316). Long-term time-sensitive costs for crf-based tracking by detection. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_4"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4484","DOI":"10.1109\/ACCESS.2016.2600623","article-title":"A temporal-spatial method for group detection, locating and tracking","volume":"4","author":"Li","year":"2016","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Guo, J., Xu, T., Shi, G., Rao, Z., and Li, X. (2017). Multi-View Structural Local Subspace Tracking. Sensors, 17.","DOI":"10.3390\/s17040666"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, D.Q., Xu, T.F., Chen, S.Y., Zhang, J.Z., and Jiang, S.W. (2016). Real-Time Tracking Framework with Adaptive Features and Constrained Labels. Sensors, 16.","DOI":"10.3390\/s16091449"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2993","DOI":"10.1016\/j.patcog.2015.04.005","article-title":"Deep feature learning with relative distance comparison for person re-identification","volume":"48","author":"Ding","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Cheng, D., Gong, Y., Zhou, S., Wang, J., and Zheng, N. (2016, January 27\u201330). Person re-identification by multi-channel parts-based cnn with improved triplet loss function. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.149"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, X., Zhang, J., and Huang, K. (2017, January 21\u201326). Beyond Triplet Loss: A deep quadruplet network for person reidentification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.145"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Batchelor, O., and Green, R. (2014, January 15\u201318). Object recognition by stochastic metric learning. Proceedings of the 10th Inter-national Conference on Simulated Evolution and Learning, Dunedin, New Zealand.","DOI":"10.1007\/978-3-319-13563-2_67"},{"key":"ref_44","unstructured":"Goldberger, J., Roweis, S., Hinton, G., and Salakhutdinov, R. (2005, January 5\u20138). Neighbourhood components analysis. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shellhamer, 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_46","first-page":"246309","article-title":"Evaluating multiple object tracking performance: The clear mot metrics","volume":"2008","author":"Keni","year":"2008","journal-title":"R. J. Image Video Process."},{"key":"ref_47","unstructured":"Huang, C., Wu, B., and Nevatia, R. (2014, January 24\u201326). Robust object tracking by hierarchical association of detection responses. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Cai, Y., and Medioni, G. (2014, January 24\u201326). Exploring context information for inter-camera multiple target tracking. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA.","DOI":"10.1109\/WACV.2014.6836026"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, W., Cao, L., Chen, X., and Huang, K. (2014, January 27\u201330). A novel solution for multi-camera object tracking. Proceedings of the 2014 IEEE International Conference on Image Processing, Paris, France.","DOI":"10.1109\/ICIP.2014.7025472"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/10\/3400\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:24:54Z","timestamp":1760196294000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/10\/3400"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,11]]},"references-count":49,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["s18103400"],"URL":"https:\/\/doi.org\/10.3390\/s18103400","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2018,10,11]]}}}