Multi Feature Path Modeling for Video Surveillance

This paper proposes a novel method for detecting nonconforming trajectories of objects as they pass through a scene. Existing methods mostly use spatial features to solve this problem. Using only spatial information is not adequate; we need to take into consideration velocity and curvature information of a trajectory along with the spatial information for an elegant solution. Our method has the ability to distinguish between objects traversing spatially dissimilar paths, or objects traversing spatially proximal paths but having different spatio-temporal characteristics. The method consists of a path building training phase and a testing phase. During the training phase, we use graph-cuts for clustering the trajectories, where the Hausdorff distance metric is used to calculate the edge weights. Each cluster represents a path. An envelope boundary and an average trajectory are computed for each path. During the testing phase we use three features for trajectory matching in a hierarchica...

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Imran N. Junejo, Mubarak Shah, Omar Javed
• Keywords: Average Trajectory, Building Training Phase, Computer Vision, ICPR 2004, Trajectory Matching