DR.VIC: Decomposition and Reasoning for Video Individual Counting

Tao Han, Lei Bai, Junyu Gao, Qi Wang, Wanli Ouyang

科研成果: 书/报告/会议事项章节会议稿件同行评审

23 引用 (Scopus)

摘要

Pedestrian counting is a fundamental tool for under-standing pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, cross-line crowd counting et al.) either only focus on the image-level counting or are constrained to the manual annotation of lines. In this work, we propose to conduct the pedes-trian counting from a new perspective - Video Individual Counting (VIC), which counts the total number of individual pedestrians in the given video (a person is only counted once). Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame. Then, an end-to-end Decomposition and Reasoning Network (DRNet) is designed to predict the initial pedestrian count with the density estimation method and reason the new pedestrian's count of each frame with the differentiable optimal transport. Extensive experiments are conducted on two datasets with congested pedestrians and diverse scenes, demonstrating the effectiveness of our method over baselines with great superiority in counting the individual pedestrians. Code: https://github.com/taohan10200/DRNet.

源语言英语
主期刊名Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
出版商IEEE Computer Society
3073-3082
页数10
ISBN(电子版)9781665469463
DOI
出版状态已出版 - 2022
活动2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, 美国
期限: 19 6月 202224 6月 2022

出版系列

姓名Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2022-June
ISSN(印刷版)1063-6919

会议

会议2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
国家/地区美国
New Orleans
时期19/06/2224/06/22

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