{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T12:17:55Z","timestamp":1780057075707,"version":"3.54.0"},"reference-count":54,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2020YFB2103705"],"award-info":[{"award-number":["2020YFB2103705"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022XFZD01"],"award-info":[{"award-number":["2022XFZD01"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["S2021Z004"],"award-info":[{"award-number":["S2021Z004"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["KYCX22_2565"],"award-info":[{"award-number":["KYCX22_2565"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022WLKXJ115"],"award-info":[{"award-number":["2022WLKXJ115"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Plan Project of Fire Department","award":["2020YFB2103705"],"award-info":[{"award-number":["2020YFB2103705"]}]},{"name":"Science and Technology Plan Project of Fire Department","award":["2022XFZD01"],"award-info":[{"award-number":["2022XFZD01"]}]},{"name":"Science and Technology Plan Project of Fire Department","award":["S2021Z004"],"award-info":[{"award-number":["S2021Z004"]}]},{"name":"Science and Technology Plan Project of Fire Department","award":["KYCX22_2565"],"award-info":[{"award-number":["KYCX22_2565"]}]},{"name":"Science and Technology Plan Project of Fire Department","award":["2022WLKXJ115"],"award-info":[{"award-number":["2022WLKXJ115"]}]},{"name":"Experimental Technology Research and Development Project of China University of Mining and Technology","award":["2020YFB2103705"],"award-info":[{"award-number":["2020YFB2103705"]}]},{"name":"Experimental Technology Research and Development Project of China University of Mining and Technology","award":["2022XFZD01"],"award-info":[{"award-number":["2022XFZD01"]}]},{"name":"Experimental Technology Research and Development Project of China University of Mining and Technology","award":["S2021Z004"],"award-info":[{"award-number":["S2021Z004"]}]},{"name":"Experimental Technology Research and Development Project of China University of Mining and Technology","award":["KYCX22_2565"],"award-info":[{"award-number":["KYCX22_2565"]}]},{"name":"Experimental Technology Research and Development Project of China University of Mining and Technology","award":["2022WLKXJ115"],"award-info":[{"award-number":["2022WLKXJ115"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["2020YFB2103705"],"award-info":[{"award-number":["2020YFB2103705"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["2022XFZD01"],"award-info":[{"award-number":["2022XFZD01"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["S2021Z004"],"award-info":[{"award-number":["S2021Z004"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX22_2565"],"award-info":[{"award-number":["KYCX22_2565"]}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["2022WLKXJ115"],"award-info":[{"award-number":["2022WLKXJ115"]}]},{"name":"Graduate Innovation Program of China University of Mining and Technology","award":["2020YFB2103705"],"award-info":[{"award-number":["2020YFB2103705"]}]},{"name":"Graduate Innovation Program of China University of Mining and Technology","award":["2022XFZD01"],"award-info":[{"award-number":["2022XFZD01"]}]},{"name":"Graduate Innovation Program of China University of Mining and Technology","award":["S2021Z004"],"award-info":[{"award-number":["S2021Z004"]}]},{"name":"Graduate Innovation Program of China University of Mining and Technology","award":["KYCX22_2565"],"award-info":[{"award-number":["KYCX22_2565"]}]},{"name":"Graduate Innovation Program of China University of Mining and Technology","award":["2022WLKXJ115"],"award-info":[{"award-number":["2022WLKXJ115"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As an essential part of intelligent monitoring, behavior recognition, automatic driving, and others, the challenge of multi-object tracking is still to ensure tracking accuracy and robustness, especially in complex occlusion environments. Aiming at the issues of the occlusion, background noise, and motion state violent change for multi-object in a complex scene, an improved DeepSORT algorithm based on YOLOv5 is proposed for multi-object tracking to enhance the speed and accuracy of tracking. Firstly, a general object motion model is devised, which is similar to the variable acceleration motion model, and a multi-object tracking framework with the general motion model is established. Then, the latest YOLOv5 algorithm, which has satisfactory detection accuracy, is utilized to obtain the object information as the input of multi-object tracking. An unscented Kalman filter (UKF) is proposed to estimate the motion state of multi-object to solve nonlinear errors. In addition, the adaptive factor is introduced to evaluate observation noise and detect abnormal observations so as to adaptively adjust the innovation covariance matrix. Finally, an improved DeepSORT algorithm for multi-object tracking is formed to promote robustness and accuracy. Extensive experiments are carried out on the MOT16 data set, and we compare the proposed algorithm with the DeepSORT algorithm. The results indicate that the speed and precision of the improved DeepSORT are increased by 4.75% and 2.30%, respectively. Especially in the MOT16 of the dynamic camera, the improved DeepSORT shows better performance.<\/jats:p>","DOI":"10.3390\/s22239106","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T03:58:16Z","timestamp":1669262296000},"page":"9106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter"],"prefix":"10.3390","volume":"22","author":[{"given":"Guowei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyao","family":"Yin","sequence":"additional","affiliation":[{"name":"Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518046, China"},{"name":"Key Laboratory of Urban Safety Risk Monitoring and Early Warning, Ministry of Emergency Management, Shenzhen 518046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Deng","sequence":"additional","affiliation":[{"name":"Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518046, China"},{"name":"Key Laboratory of Urban Safety Risk Monitoring and Early Warning, Ministry of Emergency Management, Shenzhen 518046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlong","family":"Sun","sequence":"additional","affiliation":[{"name":"Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518046, China"},{"name":"Key Laboratory of Urban Safety Risk Monitoring and Early Warning, Ministry of Emergency Management, Shenzhen 518046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518046, China"},{"name":"Key Laboratory of Urban Safety Risk Monitoring and Early Warning, Ministry of Emergency Management, Shenzhen 518046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuiyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1109\/TITS.2020.3025687","article-title":"An edge traffic flow detection scheme based on deep learning in an intelligent transportation system","volume":"22","author":"Chen","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dicle, C., Camps, O.I., and Sznaier, M. (2013, January 1\u20138). The way they move: Tracking multiple targets with similar appearance. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.286"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual object tracking using adaptive correlation filters. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-speed tracking with kernelized correlation filters","volume":"37","author":"Henriques","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F., and Felsberg, M. (2014, January 1\u20135). Accurate scale estimation for robust visual tracking. Proceedings of the British Machine Vision Conference, Nottingham, UK.","DOI":"10.5244\/C.28.65"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nam, H., and Han, B. (2016, January 27\u201330). Learning multi-domain convolutional neural networks for visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.465"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shuai, B., Berneshawi, A., Li, X., Modolo, D., and Tighe, J. (2021, January 19\u201325). Siammot: Siamese multi-object tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01219"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple online and realtime tracking with a deep association metric. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_14","unstructured":"Zuraimi, M.A.B., and Zaman, F.H.K. (2021, January 3\u20134). Vehicle detection and tracking using YOLO and DeepSORT. Proceedings of the 2021 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, S., Sheng, H., Zhang, Y., Wu, Y., and Xiong, Z. (2021, January 11\u201317). A general recurrent tracking framework without real data. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.01297"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fu, H., Wu, L., Jian, M., Yang, Y., and Wang, X. (2019, January 23\u201325). MF-SORT: Simple online and Realtime tracking with motion features. Proceedings of the International Conference on Image and Graphics, Beijing, China.","DOI":"10.1007\/978-3-030-34120-6_13"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hou, X., Wang, Y., and Chau, L.P. (2019, January 18\u201321). Vehicle tracking using deep sort with low confidence track filtering. Proceedings of the 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Taipei, Taiwan.","DOI":"10.1109\/AVSS.2019.8909903"},{"key":"ref_18","unstructured":"Luvizon, D., Tabia, H., and Picard, D. (2020). SSP-Net: Scalable Sequential Pyramid Networks for Real-Time 3D Human Pose Regression. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.Y., and Le, Q.V. (2019, January 15\u201320). Nas-fpn: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 8\u201316). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_22","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_23","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/TITS.2018.2838132","article-title":"SINet: A scale-insensitive convolutional neural network for fast vehicle detection","volume":"20","author":"Hu","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cai, Z., Fan, Q., Feris, R.S., and Vasconcelos, N. (2016, January 8\u201316). A unified multi-scale deep convolutional neural network for fast object detection. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/JOE.1983.1145560","article-title":"Sonar tracking of multiple targets using joint probabilistic data association","volume":"8","author":"Fortmann","year":"1983","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/TAC.1979.1102177","article-title":"An algorithm for tracking multiple targets","volume":"24","author":"Reid","year":"1979","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Rezatofighi, S.H., Milan, A., Zhang, Z., Shi, Q., Dick, A., and Reid, I. (2015, January 7\u201313). Joint probabilistic data association revisited. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.349"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bochinski, E., Eiselein, V., and Sikora, T. (September, January 29). High-speed tracking-by-detection without using image information. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078516"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bochinski, E., Senst, T., and Sikora, T. (2018, January 27\u201330). Extending IOU based multi-object tracking by visual information. Proceedings of the 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand.","DOI":"10.1109\/AVSS.2018.8639144"},{"key":"ref_32","unstructured":"Punn, N.S., Sonbhadra, S.K., Agarwal, S., and Rai, G. (2020). Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kapania, S., Saini, D., Goyal, S., Thakur, N., Jain, R., and Nagrath, P. (2020, January 11). Multi object tracking with UAVs using deep SORT and YOLOv3 RetinaNet detection framework. Proceedings of the 1st ACM Workshop on Autonomous and Intelligent Mobile Systems, Bangalore, India.","DOI":"10.1145\/3377283.3377284"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TPAMI.2004.53","article-title":"Support vector tracking","volume":"26","author":"Avidan","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lee, B., Erdenee, E., Jin, S., Nam, M.Y., Jung, Y.G., and Rhee, P.K. (2016, January 8\u201316). Multi-class multi-object tracking using changing point detection. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_6"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/TPAMI.2018.2884990","article-title":"A region-based gauss-newton approach to real-time monocular multiple object tracking","volume":"41","author":"Tjaden","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","unstructured":"Nam, H., Baek, M., and Han, B. (2016). Modeling and propagating cnns in a tree structure for visual tracking. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dias, R., Cunha, B., Sousa, E., Azevedo, J.L., Silva, J., Amaral, F., and Lau, N. (2017, January 26\u201328). Real-time multi-object tracking on highly dynamic environments. Proceedings of the 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Coimbra, Portugal.","DOI":"10.1109\/ICARSC.2017.7964072"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yoon, J.H., Yang, M.H., Lim, J., and Yoon, K.J. (2015, January 6\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_41","doi-asserted-by":"crossref","unstructured":"Chen, L., Ai, H., Zhuang, Z., and Shang, C. (2018, January 23\u201327). Real-time multiple people tracking with deeply learned candidate selection and person re-identification. Proceedings of the 2018 IEEE International Conference on Multimedia And Expo (ICME), San Diego, CA, USA.","DOI":"10.1109\/ICME.2018.8486597"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Al-Shakarji, N.M., Bunyak, F., Seetharaman, G., and Palaniappan, K. (2018, January 27\u201330). Multi-object tracking cascade with multi-step data association and occlusion handling. Proceedings of the 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand.","DOI":"10.1109\/AVSS.2018.8639321"},{"key":"ref_43","first-page":"584","article-title":"The unscented particle filter","volume":"13","author":"Doucet","year":"2000","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, Z., and Wei, B. (2020, January 11\u201314). A sport athlete object tracking based on deep sort and yolo V4 in case of camera movement. Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC51575.2020.9345010"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Yang, H. (2022, January 14\u201316). Multi-target Pedestrian Tracking Based on YOLOv5 and DeepSORT. Proceedings of the 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China.","DOI":"10.1109\/IPEC54454.2022.9777554"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Azhar, M.I.H., Zaman, F.H.K., Tahir, N.M., and Hashim, H. (2020, January 21\u201322). People tracking system using DeepSORT. Proceedings of the 2020 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia.","DOI":"10.1109\/ICCSCE50387.2020.9204956"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gai, Y., He, W., and Zhou, Z. (2021, January 12\u201314). Pedestrian Target Tracking Based On DeepSORT With YOLOv5. Proceedings of the 2021 2nd International Conference on Computer Engineering and Intelligent Control (ICCEIC), Chongqing, China.","DOI":"10.1109\/ICCEIC54227.2021.00008"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Zhao, N., Zhou, L., Wang, M., Yang, L., Fang, H., He, Y., and Liu, Y. (2020). Vision-based moving obstacle detection and tracking in paddy field using improved yolov3 and deep SORT. Sensors, 20.","DOI":"10.3390\/s20154082"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Jie, Y., Leonidas, L., Mumtaz, F., and Ali, M. (2021). Ship detection and tracking in inland waterways using improved YOLOv3 and Deep SORT. Symmetry, 13.","DOI":"10.3390\/sym13020308"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Parico, A.I.B., and Ahamed, T. (2021). Real time pear fruit detection and counting using YOLOv4 models and deep SORT. Sensors, 21.","DOI":"10.3390\/s21144803"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Doan, T.N., and Truong, M.T. (2020, January 12\u201314). Real-time vehicle detection and counting based on YOLO and DeepSORT. Proceedings of the 2020 12th International Conference on Knowledge and Systems Engineering (KSE), Can Tho, Vietnam.","DOI":"10.1109\/KSE50997.2020.9287483"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"12462","DOI":"10.1109\/TIE.2020.3044802","article-title":"Robust Vision-Aided Inertial Navigation System for Protection Against Ego-Motion Uncertainty of Unmanned Ground Vehicle","volume":"68","author":"Zhai","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8659","DOI":"10.1109\/TIE.2019.2946557","article-title":"A novel adaptive Kalman filtering approach to human motion tracking with magnetic-inertial sensors","volume":"67","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yoo, Y.S., Lee, S.H., and Bae, S.H. (2022). Effective Multi-Object Tracking via Global Object Models and Object Constraint Learning. Sensors, 22.","DOI":"10.3390\/s22207943"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9106\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:31Z","timestamp":1760145931000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/23\/9106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,23]]},"references-count":54,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22239106"],"URL":"https:\/\/doi.org\/10.3390\/s22239106","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,23]]}}}