Robustness Analysis of Pedestrian Detectors for Surveillance

TL;DR

This paper evaluates the robustness of pedestrian detection algorithms under video quality degradation using a new dataset and visualization tools, revealing areas for improvement in detection stability.

Contribution

It introduces a large-scale distorted video dataset, a novel robustness measure, and evaluates existing algorithms' stability against video quality issues.

Findings

Robustness varies significantly across algorithms.

Certain distortion types impact detection more severely.

The dataset enables comprehensive robustness evaluation.

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, \emph{etc}. Besides detection accuracy, a robust surveillance video system should be stable to video quality degradation by network transmission, environment variation, etc. In this study, we conduct the research on the robustness of pedestrian detection algorithms to video quality degradation. The main contribution of this work 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 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. Additionally, 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. The DSurVD data set can be download from BaiduYunDisk, https://pan.baidu.com/s/1I9Kqj8rmubOYu7bkBfkUpA, Password: lqmc