Online detection of abnormal events using incremental coding length
Abstract
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.
Publication Title
Proceedings of the National Conference on Artificial Intelligence
Recommended Citation
Dutta, J., & Banerjee, B. (2015). Online detection of abnormal events using incremental coding length. Proceedings of the National Conference on Artificial Intelligence, 3755-3761. Retrieved from https://digitalcommons.memphis.edu/facpubs/14042
