Multiple Pedestrian Tracking in Dense Crowds Combined with Head Tracking

被引:1
|
作者
Qi, Zhouming [1 ]
Zhou, Mian [1 ]
Zhu, Guoqiang [1 ]
Xue, Yanbing [1 ]
机构
[1] Tianjin Univ Technol, Sch Comp Sci & Engn, Tianjin 300384, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 01期
基金
中国国家自然科学基金;
关键词
head tracking; intersection over containment; Hungarian algorithm; deep learning;
D O I
10.3390/app13010440
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
In order to reduce the negative impact of severe occlusion in dense scenes on the performance degradation of the tracker, considering that the head is the highest and least occluded part of the pedestrian's entire body, we propose a new multiobject tracking method for pedestrians in dense crowds combined with head tracking. For each frame of the video, a head tracker is first used to generate the pedestrians' head movement tracklets, and the pedestrians' whole body bounding boxes are detected at the same time. Secondly, the degree of association between the head bounding boxes and the whole body bounding boxes are calculated, and the Hungarian algorithm is used to match the above calculation results. Finally, according to the matching results, the head bounding boxes in the head tracklets are replaced with the whole body bounding boxes, and the whole body motion tracklets of the pedestrians in the dense scene are generated. Our method can be performed online, and experiments suggested that our method effectively reduces the negative effects of false negatives and false positives on the tracker caused by severe occlusion in dense scenes.
引用
收藏
页数:18
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