Robust Pedestrian Tracking in Crowd Scenarios Using an Adaptive GMM-based Framework

Shuyang Zhang; Di Wang; Fulong Ma; Chao Qin; Zhengyong Chen; Ming Liu

IROS 2020poster3 citations

Robust Pedestrian Tracking in Crowd Scenarios Using an Adaptive GMM-based Framework

Shuyang Zhang, Di Wang, Fulong Ma, Chao Qin, Zhengyong Chen, Ming Liu

Abstract

In this paper, we address the issue of pedestrian tracking in crowd scenarios. People in close social relationships tend to act as a group which is a great challenge to individually discriminate and track pedestrians on a LiDAR system. In this paper, we integrally model groups of people and track them in a recursive framework based on Gaussian Mixture Model (GMM). The model is optimized by an extended Expectation-Maximization (EM) algorithm which can adaptively vary the number of mixture components over scans. Experimental results both qualitatively and quantitatively indicate the reliability and accuracy of our tracker in populated scenarios.

BibTeX

@inproceedings{iros2020_robustpedestrian,
  title = {Robust Pedestrian Tracking in Crowd Scenarios Using an Adaptive GMM-based Framework},
  author = {Shuyang Zhang and Di Wang and Fulong Ma and Chao Qin and Zhengyong Chen and Ming Liu},
  booktitle = {IROS 2020},
  year = {2020}
}