Smart City Gnosys
Smart city article details
| Title | Deep Learning And Multi-Modal Fusion For Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, And Comparative Study |
|---|---|
| ID_Doc | 17814 |
| Authors | Wang X.; Sun Z.; Chehri A.; Jeon G.; Song Y. |
| Year | 2024 |
| Published | Information Fusion, 105 |
| DOI | http://dx.doi.org/10.1016/j.inffus.2024.102247 |
| Abstract | Real-time multi-object tracking (MOT) is a complex task involving detecting and tracking multiple objects. After the objects are detected, they are assigned markers, and their trajectories are tracked in real-time. The scientific community is intrigued by the possibilities of utilizing MOT technology in the context of smart cities. Their primary focus lies in the domains of intelligent transportation, detection of vehicles and pedestrians, crowd surveillance, and public safety. Deep learning techniques have been developed in recent years to effectively tackle the challenges of real-time MOT tasks and enhance tracking performance. Environmental perception within smart traffic applications relies heavily on sensor data fusion. In traffic scenarios, a thoughtful approach involves utilizing a combination of sensors and cameras to detect and track targets while gathering valuable data effectively. However, it faces challenges when it comes to detecting and tracking objects that are in motion, have complex changes in appearance, or are in crowded scenes. This paper explores the foundational standard for real-time Multiple Object Tracking tasks. We prioritize the examination of quantitative measures by conducting a comprehensive analysis of widely utilized benchmark datasets and metrics. This study also investigates established embedding techniques and multi-modal fusion methods within real-time multi-target tracking algorithms. Each strategy will be classified and assessed according to a predefined set of principles. The paper presents a comprehensive analysis and visual representation of various MOT strategies. Finally, this paper aims to present an overview of the current challenges faced by the MOT mission, as well as the potential objectives that lie ahead. © 2024 Elsevier B.V. |
| Author Keywords | Deep learning; Multi-object tracking; Pedestrian detection; Smart cities; Typical baselines |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
59472  | 0.889 | Nagrath P.; Thakur N.; Jain R.; Saini D.; Sharma N.; Hemanth J. | Understanding New Age Of Intelligent Video Surveillance And Deeper Analysis On Deep Learning Techniques For Object Tracking | Internet of Things (2022) |
| 38290 | 0.887 | Zhou X.; Jia Y.; Bai C.; Zhu H.; Chan S. | Multi-Object Tracking Based On Attention Networks For Smart City System | Sustainable Energy Technologies and Assessments, 52 (2022) |
| 17929 | 0.884 | Li H.; Liu X.; Jia H.; Ahanger T.A.; Xu L.; Alzamil Z.; Li X. | Deep Learning-Based 3D Multi-Object Tracking Using Multimodal Fusion In Smart Cities | Human-centric Computing and Information Sciences, 14 (2024) |
| 38289 | 0.883 | Zhang W.; Xin Y.; Zheng C.; Peng X.; Tai J. | Multi-Object Tracking Algorithm Based On Multi-Layer Feature Adaptive Fusion | Proceedings - 2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022 (2022) |
| 23882 | 0.881 | Ahmed M.; El-Sheimy N.; Leung H. | Enhancing Object Tracking In Smart City Intelligent Transportation Systems: A Track-By-Detection Approach Utilizing Satellite Video Monitoring | 2024 IEEE International Conference on Smart Mobility, SM 2024 (2024) |
| 23868 | 0.879 | Tseng Y.-S.; Su Y.-F.; Lin D.-T. | Enhancing Multi-Target Multi-Camera Vehicle Tracking With Yolov9 And Attention Mechanisms For Smart City Traffic Monitoring | Multimedia Tools and Applications (2025) |
| 29919 | 0.876 | Yang P.; Luo X.; Sun J. | Iampdnet: Instance-Aware And Multi-Part Decoupled Network For Joint Detection And Embedding | Journal of Internet Technology, 23, 6 (2022) |
| 22093 | 0.875 | Hua S.; Anastasiu D.C. | Effective Vehicle Tracking Algorithm For Smart Traffic Networks | Proceedings - 13th IEEE International Conference on Service-Oriented System Engineering, SOSE 2019, 10th International Workshop on Joint Cloud Computing, JCC 2019 and 2019 IEEE International Workshop on Cloud Computing in Robotic Systems, CCRS 2019 (2019) |
| 5878 | 0.875 | Lin Y.; Lockyer S.; Evans A.; Zarbock M.; Zhang N. | Ablation Study For Multi-Camera Vehicle Tracking Using Cityflow Dataset | Proceedings of SPIE - The International Society for Optical Engineering, 13517 (2025) |
| 34758 | 0.871 | Wang Y.; Zhang L.; Wang X.; Ding K.; Yan J. | Large-Scale Multi-Camera Person Trajectory Tracking Based On Low Sampling Rate Of Camera | Proceedings of the International Conference on Big Data Computing and Communications, BIGCOM, 2024 (2024) |