Abstract
Automatic abnormality detection in video sequences has recently gained an increasing attention within the research community. Although progress has been seen, there are still some limitations in current research. While most systems are designed at detecting specific abnormality, others which are capable of detecting more than two types of abnormalities rely on heavy computation. Therefore, we provide a framework for detecting abnormalities in video surveillance by using multiple features and cascade classifiers, yet achieve above real-time processing speed. Experimental results on two datasets show that the proposed framework can reliably detect abnormalities in the video sequence, outperforming the current state-of-the-art methods.
Original language | English |
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Title of host publication | INSTICC : The International Conference on Computer Vision Theory and Applications |
Number of pages | 6 |
Publisher | Institute for Systems and Technologies of Information, Control and Communication |
Publication date | 2013 |
Article number | 140 |
Publication status | Published - 2013 |
Event | VISAPP 2013 - Barcelona, Spain Duration: 21 Feb 2013 → 24 Feb 2013 Conference number: 8 http://www.visapp.visigrapp.org/?y=2013 |
Conference
Conference | VISAPP 2013 |
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Number | 8 |
Country/Territory | Spain |
City | Barcelona |
Period | 21/02/2013 → 24/02/2013 |
Internet address |
Keywords
- Abnormality detection; Cascade classifier; Video surveillance; Optical flow.