Is pedestrian detection robust for surveillance?

Yuan Yuan, Weisi Lin, Yuming Fang

Abstract: In surveillance systems, pedestrian detection is a fundamental task. To improve the detection accuracy, various approaches have been proposed to address severe occlusion, pose variation, etc. However, apart from the detection accuracy, a robust surveillance system also requires stable detection performance even when the video quality degrades due to the bandwidth limitation and environment variation. To study the robustness of detection algorithms, we introduce the Distorted Surveillance Video Database (DSurVD) which includes four types of common distortions in surveillance video; we benchmark several state-of-the-art pedestrian detection algorithms on this database; miss rate index (MRI) is proposed to evaluate the performance stability of the detectors on distorted videos. Performance-Quality curves of these algorithms regarding to different types of distortion are provided. We also provide discussion on how the quality affects the detection performance.