Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.797457
Title: Crowd abnormal behaviour detection and analysis
Author: Hao, Yu
ISNI:       0000 0004 8504 1222
Awarding Body: University of Huddersfield
Current Institution: University of Huddersfield
Date of Award: 2019
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Abstract:
The analysis and understanding of abnormal behaviours in human crowds is a challenging task in pattern recognition and computer vision. First of all, the semantic definition of the term "crowd" is ambiguous. Secondly, the taxonomy of crowd behaviours is usually rudimentary and intrinsically complicated. How to identify and construct effective features for crowd behaviour classification is a prominent challenge. Thirdly, the acquisition of suitable video for crowd analysis is another critical problem. In order to address those issues, a categorization model for abnormal behaviour types is defined according to the state-of-the-art. In the novel taxonomy of crowd behaviour, eight types of crowd behaviours are defined based on the key visual patterns. An enhanced social force-based model is proposed to achieve the visual realism in crowd simulation, hence to generate customizable videos for crowd analysis. The proposed model consists of a long-term behavior control model based on A-star path finding algorithm and a short-term interaction handling model based on the enhanced social force. The proposed simulation approach produced all the crowd behaviours in the new taxonomy for the training and testing of the detection procedure. On the aspect of feature engineering, an innovative signature is devised for assisting the segmentation of crowd in both low and high density. The signature is modelled with derived features from Grey-Level Co-occurrence Matrix. Another major breakthrough is an effective approach for efficiently extracting spatial temporal information based on the information entropy theory and Gabor background subtraction. The extraction approach is capable of obtaining the texture with most motion information, which could help the detection approach to achieve the real-time processing. Overall, these contributions have supported the crucial components in a pipeline of abnormal crowd behaviour detecting process. This process is consisted of crowd behaviour taxonomy, crowd video generation, crowd segmentation and crowd abnormal behaviour detection. Experiments for each component show promising results, and proved the accessibility of the proposed approaches.
Supervisor: Xu, Zhijie Sponsor: Xi'an University of Posts and Telecommunications
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.797457  DOI: Not available
Keywords: BF Psychology
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