Online Detection of Abnormal Events in Video Streams

We propose an algorithm to handle the problem of detecting abnormal events, which is a challenging but important subject in video surveillance. The algorithm consists of an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vector machine (SVM) firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique. 1. Introduction Visual surveillance is one of the major research areas in computer vision. In a crowd image analysis problem, the scientific challenge includes abnormal events detection. For instance, Figure 1(a) illustrates a normal scene where the people are walking. In Figure 1(b), all the people are suddenly running in different directions. This dataset imitates panic-driven scenes. Figure 1: Examples of the normal and abnormal scenes: (a) Normal lawn scene: all the people are walking. (b) Abnormal lawn scene, all the people are running. Trajectory analysis of objects was described in [1–3]. The moving object was labeled by a blob in consecutive frames, and then a trajectory was produced. The deviation from the learnt trajectories was defined as abnormal events. Tracking based approaches are suitable for the sparse scenes with a few objects. The target might be lost due to occlusion. In [4, 5], abnormal detection approaches which used features encoding motion, texture, and size of the objects were introduced. Local image regions in a video were analyzed by employing background subtraction method; then a dynamic Bayesian network (DBN) was constructed to model normal and abnormal behavior, and finally a likelihood ratio test was applied to detect abnormal behaviors. In [6], a space-time Markov random field (MRF) model which detected abnormal activities in a video was proposed, mixture of probabilistic principal component analyzers (MPPCA) was adopted to model local optical flow. The prediction is based on probabilistic assumption techniques where an accurate model exists, but there are various situations where a robust and tractable model cannot be obtained; model-free methods are needed to be studied. Spatiotemporal motion features described by the context of bag of video words were adopted to detect abnormal events. In [7], the authors