稳健性(进化)
计算机科学
雷达
人工智能
计算机视觉
雷达跟踪器
跟踪(教育)
传感器融合
跟踪系统
实时计算
卡尔曼滤波器
电信
化学
基因
心理学
生物化学
教育学
作者
Fucheng Cui,Yuying Song,Jingxuan Wu,Zhouzhen Xie,Chunyi Song,Zhiwei Xu,Kai Ding
出处
期刊:IEEE Radar Conference
日期:2021-05-07
被引量:10
标识
DOI:10.1109/radarconf2147009.2021.9455185
摘要
Pedestrian trajectory tracking is crucial to ensure pedestrian safety in autonomous driving. Recently developed multi-sensor based multi-target tracking algorithms either overrely on the detection performance of some certain sensors or underutilize sensors' inherent information. Aiming at improving reliability and robustness of tracking under complex autonomous driving scenes, a new multi-sensor based tracking algorithm for pedestrians is proposed in this paper, which realizes sensor-fusion tracking by employing a newly proposed back-projection mechanism and a novel multi-hypothesis association approach. For the performance evaluation, massive experiments are performed to produce the dataset with 20 sequences. Qualitative experiment results demonstrate the superiority of the proposed algorithm over the single sensor based conventional algorithm. Quantitative experiment results further verify that the proposed algorithm reduces false negatives and improves tracking accuracy as compared to conventional multi-sensor based tracking algorithms.
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