An effective video processing pipeline for crowd pattern analysis

YU, Hao, XU, Zhijie, WANG, Jing, LIU, Ying and FAN, Jiulun (2017). An effective video processing pipeline for crowd pattern analysis. In: 2017 23rd International Conference on Automation and Computing (ICAC). IEEE.

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Abstract

With the purpose of automatic detection of crowd patterns including abrupt and abnormal changes, a novel approach for extracting motion “textures” from dynamic Spatio-Temporal Volume (STV) blocks formulated by live video streams has been proposed. This paper starts from introducing the common approach for STV construction and corresponding Spatio-Temporal Texture (STT) extraction techniques. Next the crowd motion information contained within the random STT slices are evaluated based on the information entropy theory to cull the static background and noises occupying most of the STV spaces. A preprocessing step using Gabor filtering for improving the STT sampling efficiency and motion fidelity has been devised and tested. The technique has been applied on benchmarking video databases for proof-of-concept and performance evaluation. Preliminary results have shown encouraging outcomes and promising potentials for its real-world crowd monitoring and control applications.

Item Type:	Book Section
Additional Information:	Presented at the 23rd International Conference on Automation and Computing, University of Huddersfield, Huddersfield, UK. September 07-08, 2017. IEEE Catalog Number: CFP1760R-USB
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Computing
Identification Number:	https://doi.org/10.23919/IConAC.2017.8082025
Depositing User:	Jing Wang
Date Deposited:	26 Mar 2018 12:07
Last Modified:	18 Mar 2021 06:06
URI:	https://shura.shu.ac.uk/id/eprint/18879

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