Night Time Vehicle Detection and Tracking by Fusing Vehicle Parts From Multiple Cameras

作     者：Zhang, Xinxiang Story, Brett Rajan, Dinesh 

作者机构：Southern Methodist Univ Lyle Sch Engn Dept Elect & Comp Engn Dallas TX 75205 USA Southern Methodist Univ Lyle Sch Engn Dept Civil & Environm Engn Dallas TX 75205 USA 

出 版 物：《IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS》 (IEEE Trans. Intell. Transp. Syst.)

年 卷 期：2022年第23卷第7期

页      面：8136-8156页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0814[工学-土木工程] 0823[工学-交通运输工程] 

基　　金：Electrical and Computer Engineering Department  Lyle School of Engineering  Southern Methodist University 

主　　题：Multi-camera night time vehicle detection and tracking vehicle contours vehicle representation four wheels 

摘      要：Night time vehicle detection and tracking using traditional visible light cameras is a challenging task due to the limited visibility. The current state-of-the-art systems treat the vehicles at night time as paired vehicle headlights or taillights, with no ability to determine the contour of the vehicle or its spatial occupancy. Therefore, this paper proposes the first night time framework that combines the vehicle headlights and taillights to localize the vehicle contours. This new framework includes a novel multi-camera vehicle representation that groups and reconstructs vehicle headlights and taillights following mutual geometric distances between different vehicle components. This novel vehicle contour representation successfully removes duplicated vehicle lights and also compensates for the missing vehicle lights in the detection process. Eventually, vehicle headlight alignment and contour adjustment are used to further refine the vehicle contours. The proposed multi-camera system considers typical four-wheel vehicles, e.g., cars and SUVs, in the monitoring and might not be able to handle large trucks, e.g., 18-wheelers. The experiments are conducted on night time traffic videos under various scenarios and the proposed system attains an average of 0.896 in Multiple Object Tracking Accuracy (MOTA) and an average of 0.904 in Jaccard Coefficient (JC), which indicates 19.2% and 15.9% increases over the baseline system. The night time traffic datasets used in this paper are available at https://***/JustMeZXX/Intelligent-Night-TimeTraffic-Surveillance