{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T09:24:42Z","timestamp":1784798682296,"version":"3.55.0"},"reference-count":47,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61906168"],"award-info":[{"award-number":["61906168"]}]},{"name":"National Natural Science Foundation of China","award":["62272267"],"award-info":[{"award-number":["62272267"]}]},{"name":"National Natural Science Foundation of China","award":["62102227"],"award-info":[{"award-number":["62102227"]}]},{"name":"National Natural Science Foundation of China","award":["LY23F020023"],"award-info":[{"award-number":["LY23F020023"]}]},{"name":"National Natural Science Foundation of China","award":["LZ23F020001"],"award-info":[{"award-number":["LZ23F020001"]}]},{"name":"National Natural Science Foundation of China","award":["LGF21F010002"],"award-info":[{"award-number":["LGF21F010002"]}]},{"name":"National Natural Science Foundation of China","award":["2022SDSJ01"],"award-info":[{"award-number":["2022SDSJ01"]}]},{"name":"National Natural Science Foundation of China","award":["2022LSDMIS02"],"award-info":[{"award-number":["2022LSDMIS02"]}]},{"name":"National Natural Science Foundation of China","award":["2022K91"],"award-info":[{"award-number":["2022K91"]}]},{"name":"National Natural Science Foundation of China","award":["2022AIZD0061"],"award-info":[{"award-number":["2022AIZD0061"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["61906168"],"award-info":[{"award-number":["61906168"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["62272267"],"award-info":[{"award-number":["62272267"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["62102227"],"award-info":[{"award-number":["62102227"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY23F020023"],"award-info":[{"award-number":["LY23F020023"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LZ23F020001"],"award-info":[{"award-number":["LZ23F020001"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LGF21F010002"],"award-info":[{"award-number":["LGF21F010002"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2022SDSJ01"],"award-info":[{"award-number":["2022SDSJ01"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2022LSDMIS02"],"award-info":[{"award-number":["2022LSDMIS02"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2022K91"],"award-info":[{"award-number":["2022K91"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["2022AIZD0061"],"award-info":[{"award-number":["2022AIZD0061"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["61906168"],"award-info":[{"award-number":["61906168"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["62272267"],"award-info":[{"award-number":["62272267"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["62102227"],"award-info":[{"award-number":["62102227"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["LY23F020023"],"award-info":[{"award-number":["LY23F020023"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["LZ23F020001"],"award-info":[{"award-number":["LZ23F020001"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["LGF21F010002"],"award-info":[{"award-number":["LGF21F010002"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["2022SDSJ01"],"award-info":[{"award-number":["2022SDSJ01"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["2022LSDMIS02"],"award-info":[{"award-number":["2022LSDMIS02"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["2022K91"],"award-info":[{"award-number":["2022K91"]}]},{"name":"Zhejiang Public Interest Research Project of Science and Technology Program of China","award":["2022AIZD0061"],"award-info":[{"award-number":["2022AIZD0061"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["61906168"],"award-info":[{"award-number":["61906168"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["62272267"],"award-info":[{"award-number":["62272267"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["62102227"],"award-info":[{"award-number":["62102227"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["LY23F020023"],"award-info":[{"award-number":["LY23F020023"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["LZ23F020001"],"award-info":[{"award-number":["LZ23F020001"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["LGF21F010002"],"award-info":[{"award-number":["LGF21F010002"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["2022SDSJ01"],"award-info":[{"award-number":["2022SDSJ01"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["2022LSDMIS02"],"award-info":[{"award-number":["2022LSDMIS02"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["2022K91"],"award-info":[{"award-number":["2022K91"]}]},{"name":"Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects","award":["2022AIZD0061"],"award-info":[{"award-number":["2022AIZD0061"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["61906168"],"award-info":[{"award-number":["61906168"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["62272267"],"award-info":[{"award-number":["62272267"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["62102227"],"award-info":[{"award-number":["62102227"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["LY23F020023"],"award-info":[{"award-number":["LY23F020023"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["LZ23F020001"],"award-info":[{"award-number":["LZ23F020001"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["LGF21F010002"],"award-info":[{"award-number":["LGF21F010002"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["2022SDSJ01"],"award-info":[{"award-number":["2022SDSJ01"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["2022LSDMIS02"],"award-info":[{"award-number":["2022LSDMIS02"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["2022K91"],"award-info":[{"award-number":["2022K91"]}]},{"name":"Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education","award":["2022AIZD0061"],"award-info":[{"award-number":["2022AIZD0061"]}]},{"name":"Quzhou Science and Technology Projects","award":["61906168"],"award-info":[{"award-number":["61906168"]}]},{"name":"Quzhou Science and Technology Projects","award":["62272267"],"award-info":[{"award-number":["62272267"]}]},{"name":"Quzhou Science and Technology Projects","award":["62102227"],"award-info":[{"award-number":["62102227"]}]},{"name":"Quzhou Science and Technology Projects","award":["LY23F020023"],"award-info":[{"award-number":["LY23F020023"]}]},{"name":"Quzhou Science and Technology Projects","award":["LZ23F020001"],"award-info":[{"award-number":["LZ23F020001"]}]},{"name":"Quzhou Science and Technology Projects","award":["LGF21F010002"],"award-info":[{"award-number":["LGF21F010002"]}]},{"name":"Quzhou Science and Technology Projects","award":["2022SDSJ01"],"award-info":[{"award-number":["2022SDSJ01"]}]},{"name":"Quzhou Science and Technology Projects","award":["2022LSDMIS02"],"award-info":[{"award-number":["2022LSDMIS02"]}]},{"name":"Quzhou Science and Technology Projects","award":["2022K91"],"award-info":[{"award-number":["2022K91"]}]},{"name":"Quzhou Science and Technology Projects","award":["2022AIZD0061"],"award-info":[{"award-number":["2022AIZD0061"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["61906168"],"award-info":[{"award-number":["61906168"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["62272267"],"award-info":[{"award-number":["62272267"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["62102227"],"award-info":[{"award-number":["62102227"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["LY23F020023"],"award-info":[{"award-number":["LY23F020023"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["LZ23F020001"],"award-info":[{"award-number":["LZ23F020001"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["LGF21F010002"],"award-info":[{"award-number":["LGF21F010002"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["2022SDSJ01"],"award-info":[{"award-number":["2022SDSJ01"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["2022LSDMIS02"],"award-info":[{"award-number":["2022LSDMIS02"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["2022K91"],"award-info":[{"award-number":["2022K91"]}]},{"name":"Hangzhou AI major scientific and technological innovation project","award":["2022AIZD0061"],"award-info":[{"award-number":["2022AIZD0061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multi-object tracking (MOT) is a topic of great interest in the field of computer vision, which is essential in smart behavior-analysis systems for healthcare, such as human-flow monitoring, crime analysis, and behavior warnings. Most MOT methods achieve stability by combining object-detection and re-identification networks. However, MOT requires high efficiency and accuracy in complex environments with occlusions and interference. This often increases the algorithm\u2019s complexity, affects the speed of tracking calculations, and reduces real-time performance. In this paper, we present an improved MOT method combining an attention mechanism and occlusion sensing as a solution. A convolutional block attention module (CBAM) calculates the weights of space and channel attention from the feature map. The attention weights are used to fuse the feature maps to extract adaptively robust object representations. An occlusion-sensing module detects an object\u2019s occlusion, and the appearance characteristics of an occluded object are not updated. This can enhance the model\u2019s ability to extract object features and improve appearance feature pollution caused by the short-term occlusion of an object. Experiments on public datasets demonstrate the competitive performance of the proposed method compared with the state-of-the-art MOT methods. The experimental results show that our method has powerful data association capability, e.g., 73.2% MOTA and 73.9% IDF1 on the MOT17 dataset.<\/jats:p>","DOI":"10.3390\/s23062956","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T02:01:47Z","timestamp":1678327307000},"page":"2956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-Target Tracking Based on a Combined Attention Mechanism and Occlusion Sensing in a Behavior-Analysis System"],"prefix":"10.3390","volume":"23","author":[{"given":"Xiaolong","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering at Quzhou University, Quzhou 324000, China"},{"name":"Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou 350108, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sixian","family":"Chan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"},{"name":"Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering, The College of Computer and Information at China Three Gorges University, Yichang 443002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7682-4405","authenticated-orcid":false,"given":"Chenhao","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering at Quzhou University, Quzhou 324000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tinglong","family":"Tang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Intelligent Vision-Based Monitoring for Hydroelectric Engineering, The College of Computer and Information at China Three Gorges University, Yichang 443002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"ref_1","first-page":"4","article-title":"Deep learning for health informatics","volume":"21","author":"Wong","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nasri, N., L\u00f3pez-Sastre, R.J., Pacheco-da Costa, S., Fern\u00e1ndez-Munilla, I., Guti\u00e9rrez-\u00c1lvarez, C., Pousada-Garc\u00eda, T., Acevedo-Rodr\u00edguez, F.J., and Maldonado-Basc\u00f3n, S. (2022). Assistive Robot with an AI-Based Application for the Reinforcement of Activities of Daily Living: Technical Validation with Users Affected by Neurodevelopmental Disorders. Appl. Sci., 12.","DOI":"10.3390\/app12199566"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"13738","DOI":"10.1109\/TCYB.2021.3114031","article-title":"Deep temporal model-based identity-aware hand detection for space human\u2013robot interaction","volume":"52","author":"Yu","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5171","DOI":"10.1109\/TII.2021.3122801","article-title":"Abnormal event detection using deep contrastive learning for intelligent video surveillance system","volume":"18","author":"Huang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, K., Deng, Q., Li, K., and Yu, P.S. (2019). Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans. Ind. Inform., 1\u20138.","DOI":"10.1109\/TII.2019.2909473"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Qureshi, S.A., Hussain, L., Chaudhary, Q.u.a., Abbas, S.R., Khan, R.J., Ali, A., and Al-Fuqaha, A. (2022). Kalman filtering and bipartite matching based super-chained tracker model for online multi object tracking in video sequences. Appl. Sci., 12.","DOI":"10.3390\/app12199538"},{"key":"ref_7","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, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01219"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5318","DOI":"10.1109\/TVT.2021.3062653","article-title":"Adaptive computing scheduling for edge-assisted autonomous driving","volume":"70","author":"Li","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gad, A., Basmaji, T., Yaghi, M., Alheeh, H., Alkhedher, M., and Ghazal, M. (2022). Multiple Object Tracking in Robotic Applications: Trends and Challenges. Appl. Sci., 12.","DOI":"10.3390\/app12199408"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jin, X., Zhang, J., Kong, J., Su, T., and Bai, Y. (2022). A reversible automatic selection normalization (RASN) deep network for predicting in the smart agriculture system. Agronomy, 12.","DOI":"10.3390\/agronomy12030591"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4103","DOI":"10.1109\/TIM.2019.2947125","article-title":"Enabling precision agriculture through embedded sensing with artificial intelligence","volume":"69","author":"Shadrin","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5829","DOI":"10.1109\/JBHI.2021.3137334","article-title":"Skeleton-Based Abnormal Behavior Detection Using Secure Partitioned Convolutional Neural Network Model","volume":"26","author":"Qiu","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/JBHI.2015.2445754","article-title":"Automated cognitive health assessment from smart home-based behavior data","volume":"20","author":"Dawadi","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TITS.2013.2266661","article-title":"Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis","volume":"14","author":"Sivaraman","year":"2013","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","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 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, Computer Sociey, AVSS 2017, Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078516"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6694","DOI":"10.1109\/TIP.2020.2993073","article-title":"Long-term tracking with deep tracklet association","volume":"29","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F.T., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing, ICIP 2016, Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., and Tang, X. (2014, January 23\u201328). Deep Learning Face Representation from Predicting 10, 000 Classes. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Computer Society CVPR 2014, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.244"},{"key":"ref_19","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 2017, Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, G., Yin, J., Deng, P., Sun, Y., Zhou, L., and Zhang, K. (2022). Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter. Sensors, 22.","DOI":"10.3390\/s22239106"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/978-3-030-58621-8_7","article-title":"Towards Real-Time Multi-Object Tracking","volume":"Volume 12356","author":"Vedaldi","year":"2020","journal-title":"Proceedings of the Computer Vision-ECCV 2020\u201416th European Conference"},{"key":"ref_22","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"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Boragule, A., Jang, H., Ha, N., and Jeon, M. (2022). Pixel-Guided Association for Multi-Object Tracking. Sensors, 22.","DOI":"10.3390\/s22228922"},{"key":"ref_24","unstructured":"Bergmann, P., Meinhardt, T., and Leal-Taixe, L. (November, January 27). Tracking without bells and whistles. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","article-title":"FairMOT: On the Fairness of Detection and Re-identification in Multiple Object Tracking","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (November, January 27). CenterNet: Keypoint Triplets for Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00667"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, Y., Ban, Y., Delorme, G., Gan, C., Rus, D., and Alameda-Pineda, X. (2021). TransCenter: Transformers with Dense Queries for Multiple-Object Tracking. arXiv.","DOI":"10.1109\/TPAMI.2022.3225078"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, CVPR 2018, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_29","unstructured":"Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., and Garnett, R. (2015, January 7\u201312). Spatial Transformer Networks. Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional Block Attention Module","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Proceedings of the Computer Vision-ECCV 2018\u201415th European Conference"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, CVPR 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_32","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yu, F., Wang, D., Shelhamer, E., and Darrell, T. (2018, January 18\u201322). Deep Layer Aggregation. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, Computer Society, CVPR 2018, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00255"},{"key":"ref_34","first-page":"102216","article-title":"Multi-object tracking based on attention networks for Smart City system","volume":"52","author":"Zhou","year":"2022","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7077","DOI":"10.1007\/s11042-018-6467-6","article-title":"Multi-target tracking using CNN-based features: CNNMTT","volume":"78","author":"Mahmoudi","year":"2019","journal-title":"Multim. Tools Appl."},{"key":"ref_36","unstructured":"Leal-Taix\u00e9, L., Milan, A., Reid, I.D., Roth, S., and Schindler, K. (2015). MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking. arXiv."},{"key":"ref_37","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I.D., Roth, S., and Schindler, K. (2016). MOT16: A Benchmark for Multi-Object Tracking. arXiv."},{"key":"ref_38","unstructured":"Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I.D., Roth, S., Schindler, K., and Leal-Taix\u00e9, L. (2020). MOT20: A benchmark for multi object tracking in crowded scenes. arXiv."},{"key":"ref_39","unstructured":"Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., and Sun, J. (2018). CrowdHuman: A Benchmark for Detecting Human in a Crowd. arXiv."},{"key":"ref_40","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_41","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., and Tomasi, C. Performance measures and a data set for multi-target, multi-camera tracking. Proceedings of the European Conference on Computer Vision."},{"key":"ref_42","first-page":"84","article-title":"Online Multi-target Tracking with Strong and Weak Detections","volume":"Volume 9914","author":"Hua","year":"2016","journal-title":"Lecture Notes in Computer Science Part II, Proceedings of the Computer Vision-ECCV 2016 Workshops, Amsterdam, The Netherlands, 8\u201310 October 2016"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wan, X., Wang, J., Kong, Z., Zhao, Q., and Deng, S. (2018, January 7\u201310). Multi-Object Tracking Using Online Metric Learning with Long Short-Term Memory. Proceedings of the 2018 IEEE International Conference on Image Processing, ICIP 2018, Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451174"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pang, B., Li, Y., Zhang, Y., Li, M., and Lu, C. (2020, January 13\u201319). TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, CVPR 2020, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00634"},{"key":"ref_45","first-page":"145","article-title":"Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking","volume":"Volume 12349","author":"Vedaldi","year":"2020","journal-title":"Lecture Notes in Computer Science, Part IV, Proceedings of the Computer Vision-ECCV 2020\u201416th European Conference, Glasgow, UK, 23\u201328 August 2020"},{"key":"ref_46","first-page":"104","article-title":"Deep Affinity Network for Multiple Object Tracking","volume":"43","author":"Sun","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","first-page":"474","article-title":"Tracking Objects as Points","volume":"Volume 12349","author":"Vedaldi","year":"2020","journal-title":"Lecture Notes in Computer Science, Part IV, Proceedings of the Computer Vision-ECCV 2020\u201416th European Conference, Glasgow, UK, 23\u201328 August 2020"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2956\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:51:21Z","timestamp":1760122281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/2956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,8]]},"references-count":47,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23062956"],"URL":"https:\/\/doi.org\/10.3390\/s23062956","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,8]]}}}