TY - GEN
T1 - Robust pedestrian tracking in crowd scenarios using an adaptive GMM-based framework
AU - Zhang, Shuyang
AU - Wang, Di
AU - Ma, Fulong
AU - Qin, Chao
AU - Chen, Zhengyong
AU - Liu, Ming
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85102395040
U2 - 10.1109/IROS45743.2020.9341463
DO - 10.1109/IROS45743.2020.9341463
M3 - 会议稿件
AN - SCOPUS:85102395040
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 9992
EP - 9998
BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Y2 - 24 October 2020 through 24 January 2021
ER -