Enhancing and Dissecting Crowd Counting by Synthetic Data

作者：Yi Hou, Chengyang Li, Yuheng Lu, Liping Zhu, Yuan Li, Huizhu Jia, Xiaodong Xie · 发表于：ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 年份：2022 · DOI：10.1109/icassp43922.2022.9747070 · 被引用次数：15 · 研究领域：Video Surveillance and Tracking Methods、Anomaly Detection Techniques and Applications、Human Pose and Action Recognition

In this article, we propose a simulated crowd counting dataset CrowdX, which has a large scale, accurate labeling, parameterized realization, and high fidelity. The experimental results of using this dataset as data enhancement show that the performance of the proposed streamlined and efficient benchmark network ESA-Net can be improved by 8.4%. The other two classic heterogeneous architectures MCNN and CSRNet pre-trained on CrowdX also show significant performance improvements. Considering many influencing factors determine performance, such as background, camera angle, human density, and resolution. Although these factors are important, there is still a lack of research on how they affect crowd counting. Thanks to the CrowdX dataset with rich annotation information, we conduct a large number of data-driven comparative experiments to analyze these factors. Our research provides a reference for a deeper understanding of the crowd counting problem and puts forward some useful suggestions in the actual deployment of the algorithm.