{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:41:34Z","timestamp":1760240494931,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,7,9]],"date-time":"2019-07-09T00:00:00Z","timestamp":1562630400000},"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>There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.<\/jats:p>","DOI":"10.3390\/s19133016","type":"journal-article","created":{"date-parts":[[2019,7,10]],"date-time":"2019-07-10T03:05:26Z","timestamp":1562727926000},"page":"3016","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3014-8095","authenticated-orcid":false,"given":"Ruey-Kai","family":"Sheu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Tunghai University, Taichung 40704, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8144-0734","authenticated-orcid":false,"given":"Mayuresh","family":"Pardeshi","sequence":"additional","affiliation":[{"name":"Electrical Engineering and Computer Science Department (EECS-IGP), National Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8449-7872","authenticated-orcid":false,"given":"Lun-Chi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tunghai University, Taichung 40704, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3621-9528","authenticated-orcid":false,"given":"Shyan-Ming","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Chiao Tung University, Hsinchu 30010, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Natarajan, P., Atrey, P.K., and Kankanhalli, M. (2015). Multi-Camera Coordination and Control in Surveillance Systems: A Survey. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM), 11.","DOI":"10.1145\/2710128"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s10462-017-9545-7","article-title":"Suspicious human activity recognition: A review","volume":"50","author":"Tripathi","year":"2018","journal-title":"Artif. Intell. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Akdemir, U., Turaga, P., and Chellappa, R. (2008, January 26\u201331). An ontology based approach for activity recognition from video. Proceedings of the 16th ACM International Conference on Multimedia, Vancouver, BC, Canada.","DOI":"10.1145\/1459359.1459466"},{"key":"ref_4","unstructured":"Chuang, C.H., Hsieh, J.W., Tsai, L.W., Ju, P.S., and Fan, K.C. (2008, January 18\u201321). Suspicious object detection using fuzzy-color histogram. Proceedings of the IEEE International Symposium on Circuits and Systems, Seattle, WA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1109\/TCSVT.2009.2017415","article-title":"Carried object detection using ratio histogram and its application to suspicious event analysis","volume":"19","author":"Chuang","year":"2009","journal-title":"IEEE Trans. Circuit Syst. Video Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s11263-010-0355-5","article-title":"Stochastic representation and recognition of high-level group activities","volume":"93","author":"Ryoo","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_7","unstructured":"Ibrahim, N., Mokri, S.S., Siong, L.Y., Mustafa, M.M., and Hussain, A. (July, January 30). Snatch theft detection using low level. Proceedings of the World Congress on Engineering, London, UK."},{"key":"ref_8","first-page":"6068","article-title":"Crime detection and avoidance in ATM: A new framework","volume":"5","author":"Sujith","year":"2014","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1049\/ip-vis:20041147","article-title":"Intelligent distributed surveillance systems: A review","volume":"152","author":"Valera","year":"2005","journal-title":"IEE Proc. Vision Image Signal. Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1109\/TCSVT.2008.927109","article-title":"A survey of vision-based trajectory learning and analysis for surveillance","volume":"18","author":"Morris","year":"2008","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1456650.1456657","article-title":"Survey and analysis of multimodal sensor planning and integration for wide area surveillance","volume":"41","author":"Abidi","year":"2008","journal-title":"ACM Comput. Surv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Javed, O., and Mubarak, S. (2008). Automated Multi-Camera Surveillance: Algorithms and Practice. Image and Video Processing, Springer.","DOI":"10.1007\/978-0-387-78881-4"},{"key":"ref_13","unstructured":"Aghajan, H., and Cavallaro, A. (2009). Multi-Camera Networks Principles and Applications, Academic Press. [1st ed.]."},{"key":"ref_14","unstructured":"Kim, H., and Wolf, M. (September, January 31). Distributed tracking in a large-scale network of smart cameras. Proceedings of the 4th ACM\/IEEE International Conference on Distributed Smart Cameras, Atlanta, GA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/SURV.2011.102910.00098","article-title":"Towards efficient wireless video sensor networks: A survey of existing node architectures and proposal for a flexi-WVSNP design","volume":"13","author":"Seema","year":"2011","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_16","first-page":"1","article-title":"Camera networks: The acquisition and analysis of videos over wide areas","volume":"3","author":"Song","year":"2012","journal-title":"Synth. Lect. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s11042-011-0840-z","article-title":"A survey of visual sensor network platforms","volume":"60","author":"Tavli","year":"2012","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2543581.2543596","article-title":"People reidentification in surveillance and forensics: A survey","volume":"46","author":"Vezzani","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_19","unstructured":"Song, M., Tao, D., and Maybank, S.J. (2013). Sparse Camera Network for Visual Surveillance: A Comprehensive Survey. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tan, Y., Tai, Y., and Xiong, S. (2018). NCA-Net for Tracking Multiple Objects across Multiple Cameras. Sensors, 18.","DOI":"10.3390\/s18103400"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2012.07.005","article-title":"Intelligent multi-camera video surveillance: A review","volume":"34","author":"Wang","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2545883","article-title":"Security and privacy protection in visual sensor networks: A survey","volume":"47","author":"Winkler","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/MC.2014.133","article-title":"Self-reconfigurable smart camera networks","volume":"47","author":"SanMiguel","year":"2014","journal-title":"Computer"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11263-007-0075-7","article-title":"Incremental Learning for Robust Visual Tracking","volume":"77","author":"Ross","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Luo, J., Zhang, J., Zi, C., Niu, Y., Tian, H., and Xiu, C. (2015). Gait Recognition using GEI and AFDEI. Int. J. Opt., 2015.","DOI":"10.1155\/2015\/763908"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0004-3702(85)90084-0","article-title":"Depth-First Iterative Deepening: An Optimal Admissible Tree Search","volume":"27","author":"Korf","year":"1985","journal-title":"Artif. Intell."},{"key":"ref_27","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement, Computer Vision and Pattern Recognition. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Bevridge, J.R., Draper, B., and Lui, Y.M. (2010, January 13\u201318). Visual Object Tracking using Adaptive Correlation Filters. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_29","unstructured":"Kosub, S. (2016). A Note on the Triangle Inequality for the Jaccard Distance. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., and Sheikh, Y. (2018). OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. arXiv.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_31","unstructured":"Nwankpa, C., Ijomah, W., Gachagan, A., and Marshall, S. (2018). Activation Functions: Comparison of Trends in Practice and Research for Deep Learning. arXiv."},{"key":"ref_32","unstructured":"Feng, Y., Li, Y., and Luo, J. (2016, January 4\u20138). Learning Effective Gait Features Using LSTM. Proceedings of the 2016 23rd International Conference on Pattern Recognition, Cancun, Mexico."},{"key":"ref_33","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., and Greer, K. (2013). KNN Model Based Approach in Classification, Springer. Lecture Notes in Computer Science."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gou, M., Karanam, S., Liu, W.Q., Camps, O.I., and Radke, R.J. (2017, January 21\u201326). DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.185"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zheng, L., Bie, Z., Sun, Y., Wang, J., and Wang, S. (2016). MARS: A Video Benchmark for Large-Scale Person Re-Identification, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46466-4_52"},{"key":"ref_36","unstructured":"Leal-Taixe, L., Milan, A., Reid, I., Roth, S., and Schindler, K. (2015). MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking. arXiv."},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"246309","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_39","doi-asserted-by":"crossref","unstructured":"Bewley, A., Zongyuan, G., Ramos, F., and Upcroft, B. (2016). Simple Online and Realtime Tracking. arXiv.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yang, M., and Jia, Y. (2015). Temporal Dynamic Appearance Modeling for Online Multi-Person Tracking. arXiv.","DOI":"10.1016\/j.cviu.2016.05.003"},{"key":"ref_41","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_42","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 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.12"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bae, S.H., and Yoon, K.J. (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_44","doi-asserted-by":"crossref","unstructured":"Zhang, S., Staudt, E., Faltemier, T., and Roy-Chowdhury, A.K. (2015, January 5\u20139). A camera network tracking (camnet) dataset and performance baseline. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2015.55"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Delforouzi, A., Pamarthi, B., and Grzegorzek, M. (2018). Training-Based Methods for Comparison of Object Detection Methods for Visual Object Tracking. Sensors, 18.","DOI":"10.3390\/s18113994"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ali, A., and Mirza, S.M. (2006, January 13\u201314). Object Tracking using Correlation, Kalman Filter and Fast Means Shift Algorithms. Proceedings of the 2006 International Conference on Emerging Technologies, Peshawar, Pakistan.","DOI":"10.1109\/ICET.2006.335916"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/3016\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:03:53Z","timestamp":1760187833000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/13\/3016"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,9]]},"references-count":46,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19133016"],"URL":"https:\/\/doi.org\/10.3390\/s19133016","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,7,9]]}}}