Abstract
Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television (CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gabor-filtered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns (signatures) is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.
Highlights
Closed-circuit television (CCTV) cameras are widely installed in city centers, along main roads and highways, fixed and/or moving locations inside stadiums, concert halls, shopping malls, and other key installations for ensuring public welfare and safety
Because automatic classification of crowd patterns includes abrupt and abnormal changes, a novel approach for extracting motion “textures” from dynamic spatio-temporal volumes (STV) blocks formulated by live video streams has been proposed
The crowd motion information contained within the random spatio-temporal texture (STT) slices is evaluated based on the information entropy theory to cull the static background and noises occupying most of the STV spaces
Summary
Closed-circuit television (CCTV) cameras are widely installed in city centers, along main roads and highways, fixed and/or moving locations inside stadiums, concert halls, shopping malls, and other key installations for ensuring public welfare and safety. The detection of an individual behavior, or actions, among other crowd entities becomes a focus, and poses a challenging question, especially when crowd density is high. Yan et al.[10] proposed a technique using SFM to detect sudden changes in crowd behavior In this approach, the interaction force in SFM is directly calculated from the code stream to increase efficiency, the BoW algorithm is applied to generate histograms on intensity and angles of interaction force flow. Ji et al.[16] introduced an approach using the combination of local spatio-temporal features and global positional distribution information to extract 3-dimensional (3D) scale-invariant feature transform (SIFT) descriptors on detected points-of-interest. This paper is organized as follows: Section 2 introduces a novel model for identifying and extracting spatial-temporal textures (STT) from video footage
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