Detecting rare events using Kullback-Leibler divergence


Autoria(s): Xu, Jingxin; Denman, Simon; Fookes, Clinton B.; Sridharan, Sridha
Data(s)

19/04/2015

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/80278/

Publicador

IEEE

Relação

http://eprints.qut.edu.au/80278/1/ICASSP.pdf

DOI:10.1109/ICASSP.2015.7178181

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.

Fonte

School of Electrical Engineering & Computer Science; Science & Engineering Faculty

Palavras-Chave #080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING #080104 Computer Vision #090609 Signal Processing #Video Surveillance #Event Detection
Tipo

Conference Paper