An Improved Method of Crowd Counting Based on Regression

DOI

10.2991/icmt-13.2013.18How to use a DOI?

Keywords

Crowd counting·Feature extraction·Relevance vector regression (RVR)·Gaussian process regression (GPR)

Abstract

An improved method of crowd counting based on regression is proposed to support intelligent management over crowd in video surveillance systems. According to the fact that human body has an articulate structure and complicated contours of shape, we propose a new low-level feature, the number of corner points, to highlight the describable capability of the feature set. We then introduce relevance vector regression (RVR) to model the correspondence between features and the pedestrian number, and propose a fusion scheme of RVR and Gaussian process regression (GPR) to further advance the performance of the proposed algorithm. Experimental results on two crowd datasets (one is UCSDpeds) demonstrate that the proposed work outperforms state-of-the-art methods and can fulfill the real-time requirement.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiang Mei
AU  - Zhao Yanyun
PY  - 2013/11
DA  - 2013/11
TI  - An Improved Method of Crowd Counting Based on Regression
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 143
EP  - 150
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.18
DO  - 10.2991/icmt-13.2013.18
ID  - Mei2013/11
ER  -