Online Detection of Abnormal Events Using Incremental Coding Length


  • Jayanta Dutta University of Memphis
  • Bonny Banerjee University of Memphis



abnormal event detection, online sparse coding, incremental coding length, rarity


We present an unsupervised approach for abnormal event detection in videos. We propose, given a dictionary of features learned from local spatiotemporal cuboids using the sparse coding objective, the abnormality of an event depends jointly on two factors: the frequency of each feature in reconstructing all events (or, rarity of a feature) and the strength by which it is used in reconstructing the current event (or, the absolute coefficient). The Incremental Coding Length (ICL) of a feature is a measure of its entropy gain. Given a dictionary, the ICL computation does not involve any parameter, is computationally efficient and has been used for saliency detection in images with impressive results. In this paper, the rarity of a dictionary feature is learned online as its average energy, a function of its ICL. The proposed approach is applicable to real world streaming videos. Experiments on three benchmark datasets and evaluations in comparison with a number of mainstream algorithms show that the approach is comparable to the state-of-the-art.




How to Cite

Dutta, J., & Banerjee, B. (2015). Online Detection of Abnormal Events Using Incremental Coding Length. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).