[1]QI Pengyu,WANG Hongyuan,ZHANG Ji,et al.Crowded pedestrian detection algorithm based on improved FCOS[J].CAAI Transactions on Intelligent Systems,2021,16(4):811-818.[doi:10.11992/tis.202010012]
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Crowded pedestrian detection algorithm based on improved FCOS

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 4 Page number: 811-818 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2021-07-05
Title:
Crowded pedestrian detection algorithm based on improved FCOS
Author(s):
QI Pengyu;  WANG Hongyuan;  ZHANG Ji;  ZHU Fan;  XU Zhichen
School of Information Science and Engineering, Changzhou University, Changzhou 213164, China
Keywords:
pedestrian detection; multi-scale detection; fully convolutional one-stage object detection; crowded pedestrian scene; training strategy; small object detection; scale regression; pixel by pixel prediction
CLC:
TP391.41
DOI:
10.11992/tis.202010012
Abstract:
In view of the detection difficulty resulting from small pedestrian objects, pedestrian occlusion, and pedestrian overlap in large-scale crowded scene videos, this study applies a pixel-by-pixel prediction object detection framework, i.e., fully convolutional one-stage object detection (FCOS), for pedestrian detection. An improved backbone network is proposed to extract pedestrian features, achieve multi-scale detection of object pedestrians by increasing scale regression, reduce the number of objects detected by other feature layers, and thereby improve the ability of pedestrian detection. Several experiments have been performed on the crowded pedestrian scene dataset CrowdHuman and the small object pedestrian dataset Caltech. The results show that compared with current advanced methods, the proposed algorithm makes some improvements in the pedestrian detection accuracy, especially for small object pedestrian detection. Compared with the original FCOS framework, the average precision on CrowdHuman is increased by nearly 15% and the miss rate is decreased by nearly 33.0%. The average precision on Caltech is increased by 2%. Moreover, the actual use in complex, crowded scenarios proves the effectiveness of this algorithm.
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