Issue No. 01 - Jan. (2014 vol. 36)
Weixin Li , Univ. of California, San Diego, La Jolla, CA, USA
Vijay Mahadevan , Univ. of California, San Diego, La Jolla, CA, USA
Nuno Vasconcelos , Univ. of California, San Diego, La Jolla, CA, USA
The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results.
Hidden Markov models, Computer vision, Image motion analysis, Computational modeling, Detectors, Feature extraction, Principal component analysis
Weixin Li, V. Mahadevan and N. Vasconcelos, "Anomaly Detection and Localization in Crowded Scenes," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 36, no. 1, pp. 18-32, 2013.