Unusual event detection in crowded scenes

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


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:

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

97 since deposited on 20 Oct 2014
37 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

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

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page