Euclidean path modeling for video surveillance

摘要

In this paper, we address the issue of Euclidean path modeling in a single camera for activity monitoring in a multi-camera video surveillance system. The method consists of a path building training phase and a testing phase. During the unsupervised training phase, after auto-calibrating a camera and thereafter metric rectifying the input trajectories, a weighted graph is constructed with trajectories represented by the nodes, and weights determined by a similarity measure. Normalized-cuts are recursively used to partition the graph into prototype paths. Each path, consisting of a partitioned group of trajectories, is represented by a path envelope and an average trajectory. For every prototype path, features such as spatial proximity, motion characteristics, curvature, and absolute world velocity are then recovered directly in the rectified images or by registering to aerial views. During the testing phase, using our simple yet efficient similarity measures for these features, we seek a relation between the trajectories of an incoming sequence and the prototype path models to identify anomalous and unusual behaviors. Real-world pedestrian sequences are used to evaluate the steps, and demonstrate the practicality of the proposed approach.