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. [Book Section]

Documents
18879:401887
[thumbnail of ICAC17-1.pdf]
Preview
PDF
ICAC17-1.pdf - Accepted Version
Available under License All rights reserved.

Download (1MB) | Preview
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.
More Information
Statistics

Downloads

Downloads per month over past year

Metrics

Altmetric Badge

Dimensions Badge

Share
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Actions (login required)

View Item View Item