Learning Rare Behaviours

Jian Li, Timothy M. Hospedales, Shaogang Gong, Tao Xiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel approach to detect and classify rare behaviours which are visually subtle and occur sparsely in the presence of overwhelming typical behaviours. We treat this as a weakly supervised classification problem and propose a novel topic model: Multi-Class Delta Latent Dirichlet Allocation which learns to model rare behaviours from a few weakly labelled videos as well as typical behaviours from uninteresting videos by collaboratively sharing features among all classes of footage. The learned model is able to accurately classify unseen data. We further explore a novel method for detecting unknown rare behaviours in unseen data by synthesising new plausible topics to hypothesise any potential behavioural conflicts. Extensive validation using both simulated and real-world CCTV video data demonstrates the superior performance of the proposed framework compared to conventional unsupervised detection and supervised classification approaches.
Original languageEnglish
Title of host publicationComputer Vision - ACCV 2010 - 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part II
PublisherSpringer Berlin Heidelberg
Pages293-307
Number of pages15
ISBN (Electronic)978-3-642-19309-5
ISBN (Print)978-3-642-19308-8
DOIs
Publication statusPublished - 2010

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer Berlin Heidelberg
Volume6493
ISSN (Print)0302-9743

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