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. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Li, J., Hospedales, T. M., Gong, S., & Xiang, T. (2011). Learning rare behaviours. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6493 LNCS, pp. 293–307). https://doi.org/10.1007/978-3-642-19309-5_23
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