Robust Target Detection and Tracking Algorithm Based on Roadside Radar and Camera

被引:17
|
作者
Bai, Jie [1 ]
Li, Sen [1 ]
Zhang, Han [1 ]
Huang, Libo [1 ]
Wang, Ping [2 ]
机构
[1] Tongji Univ, Inst Intelligent Vehicles, Sch Automot Studies, Shanghai 201804, Peoples R China
[2] Tongji Univ, Coll Elect Sci & Technol, Shanghai 201804, Peoples R China
关键词
target detection and tracking; sensor fusion; roadside radar and camera; intelligent transportation system; SYSTEM; VISION;
D O I
10.3390/s21041116
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors' optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.
引用
收藏
页码:1 / 23
页数:23
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