Section: New Results

Anomaly detection using block-based histograms of crowd motion patterns

Participants : Juan Perez Rua, Antoine Basset, Patrick Bouthemy.

We have developed a new and generic method to detect and localize abnormal events in videos of crowd scenes. The algorithm consists first in determining the flow vector and crowd motion class for every moving pixel from a set of affine motion models estimated on a collection of windows. Then, the observed scene is subdivided in blocks to compute crowd motion class histograms weighted by the motion vector magnitudes. A very simple training step enables to get the reference histograms per block accounting for the normal behaviours. For each block, we can automatically set by means of statistical arguments the threshold on the distance between the histogram in the current image and the reference histogram that decides the presence of an abnormal event in that block. Results of extensive experimentation on different types of anomaly datasets show that our method is competitive with respect to methods relying on far more elaborated models on both appearance and motion and thus involving a significant learning stage. It outperforms any other existing purely motion-based anomaly localization method.