Detecting rare events using Kullback-Leibler divergence

Jingxin Xu; Simon Denman; Clinton Fookes; Sridha Sridharan

ICASSP 2015accepted0 citations

Detecting rare events using Kullback-Leibler divergence

Jingxin Xu, Simon Denman, Clinton Fookes, Sridha Sridharan

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.

BibTeX

@inproceedings{icassp2015_detectingrareeve,
  title = {Detecting rare events using Kullback-Leibler divergence},
  author = {Jingxin Xu and Simon Denman and Clinton Fookes and Sridha Sridharan},
  booktitle = {ICASSP 2015},
  year = {2015}
}