Learning a scene contextual model for tracking and abnormality detection

Abstract

In this paper we present a novel framework for learning contextual motion model involving multiple objects in far-field surveillance video and apply the learned model to improving the performance of objects tracking and abnormal event detection. We represent trajectory of multiple objects by a 3D graph G in x,y,t, which is augmented by a number of spatio-temporal relations (links) between moving and static objects in the scene (e.g. relation between crosswalk, pedestrian and car). An inhomogeneous Markov model p is defined over G, whose parameters are estimated by MLE method and relations are pursued by a minimax entropy principle (as in texture modeling) [16] so that we can synthesize entirely new video sequences that reproduce the observed statistics from training video. With the learned model, we define the abnormality of a subgraph given its neighborhood by log-likelihood ratio test, which is estimated by importance sampling. The learned model is applied to tracking and abnormal event detection. Our experiments show that the learned model improve tracking performance and detect sophisticated abnormal events like traffic rule violation.