Unusual event detection in crowded scenes

Xu, Jingxin (2014) Unusual event detection in crowded scenes. PhD thesis, Queensland University of Technology.

Abstract

Novel computer vision techniques have been developed to automatically detect unusual events in crowded scenes from video feeds of surveillance cameras. The research is useful in the design of the next generation intelligent video surveillance systems. Two major contributions are the construction of a novel machine learning model for multiple instance learning through compressive sensing, and the design of novel feature descriptors in the compressed video domain.

Impact and interest:

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Full-text downloads:

89 since deposited on 20 Oct 2014
44 in the past twelve months

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ID Code: 76365
Item Type: QUT Thesis (PhD)
Supervisor: Sridharan, Sridha, Yarlagadda, Prasad, Denman, Simon, & Fookes, Clinton
Keywords: Video Surveillance, Event Detection, Signal Processing, Pattern Recognition, Machine Learning, Compressive Sensing, Video Compression, Closed Circuits Television, Security Camera Networks, Bayesian Networks
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 20 Oct 2014 05:00
Last Modified: 03 Sep 2015 05:09

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