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Crowd Behavior Understanding through SIOF Feature Analysis

Lu, Li, He, Jianhua, Xu, Zhijie, Xu, Yuanping, Zhang, Chaolong, Wang, Jing and Adu, Jianhua (2017) Crowd Behavior Understanding through SIOF Feature Analysis. Proceedings of the 23rd International Conference on Automation & Computing, (University of Huddersfield, 7-8 September 2017).

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Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over time. A non-linear SVM has then been adopted to classify dominant or large-scale crow d abnormal behaviors. The work reported has focused on: 1) online (or near real-time) detection of moving objects through a background subtraction model, namely ViBe; and to identify the saliency information as a spatial feature in addition to the optical flow of the motion foreground as the temporal feature; 2) to combine the extracted spatial and temporal features into a novel SIOF descriptor that encapsulates the global movement characteristic of a crowd; 3) the optimization of a nonlinear support vector machine (SVM) as classifier to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the BEHAVE database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements in terms of the accuracy and efficiency for detecting crowd anomalies.

Item Type: Article
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Crowd analysis; SIOF; Optical flow; Support Vector Machine; Anomaly detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering > High-Performance Intelligent Computing > Visualisation, Interaction and Vision
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Depositing User: Zhijie Xu
Date Deposited: 10 Oct 2017 08:24
Last Modified: 28 Aug 2021 15:31


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