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- Title
Multi-Object Tracking Model Based on Detection Tracking Paradigm in Panoramic Scenes.
- Authors
Shen, Jinfeng; Yang, Hongbo
- Abstract
Featured Application: With the rapid advancements in artificial intelligence, Multi-Object Tracking algorithms play a crucial role in domains such as intelligent transportation, urban surveillance, and wildlife conservation. Investigating these algorithms in panoramic scenarios can significantly broaden their applicability, offering substantial societal value. Multi-Object Tracking (MOT) technology is dedicated to continuously tracking multiple targets of interest in a sequence of images and accurately identifying their specific positions at different times. This technology is crucial in key application areas such as autonomous driving and security surveillance. However, the application process often requires the coordination of cameras from multiple angles for tracking. Directly studying Multi-Object Tracking algorithms in panoramic scenes is an effective way to address this issue. The uniqueness of panoramic scenes causes target position changes at the boundaries and tracking difficulties due to continuous changes in target scales. To ensure the accuracy of target tracking, this study explores a detection-based tracking method using the newly improved YOLOx detector and the adjusted DeepSORT algorithm. Firstly, YOLOx_s was chosen as the detector because its simple network structure ensures a fast computational speed. During the feature extraction stage, we used the Polarized Self-Attention (PSA) mechanism to capture more feature information, thereby improving the tracking performance on small-scale targets. Secondly, the tracker was improved by adding a camera motion compensation module before predicting the target's position to mitigate the impact of camera shake on tracking. Finally, to address the difficulty of continuously tracking targets in specific areas of panoramic scenes, this study proposes specific tracking strategies. These strategies effectively resolve the problem of tracking failure caused by target position changes at the boundaries. Experimental results show that improved algorithms have a superior performance on multiple evaluation metrics compared to other algorithms in the field. Compared to the original algorithm, the improved algorithm exhibits a 6% increase in the quantitative metric MOTA, a 7% increase in IDF1, and a 40% decrease in IDSWs, demonstrating its leading performance.
- Subjects
TRACKING radar; OBJECT tracking (Computer vision); TRACKING algorithms; IMAGE stabilization; MULTIPLE target tracking; WILDLIFE conservation; FEATURE extraction
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 10, p4146
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14104146