Detecting rare events using Kullback-Leibler divergence

Xu, Jingxin, Denman, Simon, Fookes, Clinton B., & Sridharan, Sridha (2015) Detecting rare events using Kullback-Leibler divergence. In Proceedings of the 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015, IEEE, Brisbane, QLD, pp. 1305-1309.

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Abstract

One main challenge in developing a system for visual surveillance event detection is the annotation of target events in the training data. By making use of the assumption that events with security interest are often rare compared to regular behaviours, this paper presents a novel approach by using Kullback-Leibler (KL) divergence for rare event detection in a weakly supervised learning setting, where only clip-level annotation is available. It will be shown that this approach outperforms state-of-the-art methods on a popular real-world dataset, while preserving real time performance.

Impact and interest:

2 citations in Scopus
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ID Code: 80278
Item Type: Conference Paper
Refereed: No
Additional URLs:
Keywords: Video Surveillance, Event Detection
DOI: 10.1109/ICASSP.2015.7178181
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 20 Jan 2015 03:24
Last Modified: 12 Sep 2015 04:11

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