Video Detection and Fusion Tracking for the Targets in Traffic Scenario
Authors: 
Pan Li, Jianing Zhang, Chengxi Han, Luping Xu, Baoguo Feng, Yongjun Zhou, Sen Zhang, Bo Zhang Academic Editor: Iickho SongAuthors Info & Claims
Pan Li, Jianing Zhang, Chengxi Han, Luping Xu, Baoguo Feng, Yongjun Zhou, Sen Zhang, Bo Zhang Academic Editor: Iickho SongAuthors Info & ClaimsAbstract
In smart transportation systems, obtaining timely and accurate information about the location, speed, category, and shape of traffic targets requires more than what a single sensor can offer. This shortfall has prompted increased attention and rapid development of fusion systems centered around radar and video. Radar, while effective at measuring speed, often faces issues such as false tracks, redundant tracks, and low visibility in tracking traffic targets. On the other hand, video provides high visualization but struggles with problems such as target adhesion, low detection confidence, and difficulty in speed measurement. The complementary nature of these technologies underscores the importance of fusion systems in addressing the limitations of individual sensors to improve the accuracy and reliability of smart transportation systems. This paper introduces a fusion tracking method that combines radar and video data for tracking traffic targets. By leveraging the target’s morphological information at various pixel positions, this method supplements the radar data, thereby enhancing the accuracy of data association. The experimental results demonstrate that the association algorithm for optimal data fusion proposed in this paper improves performance by 3.86% and 2.13% compared with the traditional fusion method and the IOU matching fusion, respectively. In addition, it shows greater resilience to complex environments.
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- Video Detection and Fusion Tracking for the Targets in Traffic Scenario
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Copyright © 2024 Pan Li et al.
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Published: 01 January 2024
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