Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87575
Title: Robustness analysis of pedestrian detectors for surveillance
Authors: Fang, Yuming
Ding, Guanqun
Yuan, Yuan
Lin, Weisi
Liu, Haiwen
Keywords: Object Detection
Video Surveillance
Issue Date: 2018
Source: Fang, Y., Ding, G., Yuan, Y., Lin, W., & Liu, H. (2018). Robustness analysis of pedestrian detectors for surveillance. IEEE Access, 6, 28890-28902.
Series/Report no.: IEEE Access
Abstract: To obtain effective pedestrian detection results in surveillance video, there have been many methods proposed to handle the problems from severe occlusion, pose variation, clutter background, and so on. Besides detection accuracy, a robust surveillance video system should be stable to video quality degradation by network transmission, environment variation, and so on. In this paper, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation. The main contribution of this paper includes the following three aspects. First, a large-scale distorted surveillance video data set (DSurVD) is constructed from high-quality video sequences and their corresponding distorted versions. Second, we design a method to evaluate detection stability and a robustness measure called robustness quadrangle, which can be adopted to the visualize detection accuracy of pedestrian detection algorithms on high-quality video sequences and stability with video quality degradation. Third, the robustness of seven existing pedestrian detection algorithms is evaluated by the built DSurVD. Experimental results show that the robustness can be further improved for existing pedestrian detection algorithms. In addition, we provide much in-depth discussion on how different distortion types influence the performance of pedestrian detection algorithms, which is important to design effective pedestrian detection algorithms for surveillance.
URI: https://hdl.handle.net/10356/87575
http://hdl.handle.net/10220/45447
DOI: 10.1109/ACCESS.2018.2840329
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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