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Publication . Conference object . Part of book or chapter of book . 2017

An Effective Video Processing Pipeline for Crowd \ud Pattern Analysis

Yu Hao; Zhijie Xu; Jing Wang; Ying Liu; Jiulun Fan;
Open Access
Published: 26 Oct 2017
Publisher: IEEE
Country: United Kingdom

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.

Subjects by Vocabulary

ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

Microsoft Academic Graph classification: Video processing Preprocessor Pipeline transport Computer vision Fidelity media_common.quotation_subject media_common Benchmarking Pattern analysis Artificial intelligence business.industry business Feature extraction Entropy (information theory) Computer science


QA75, QA76