A Particle Filter Based Sequential Trajectory Classifier For Behavior Analysis In Video Surveillance

The problem of behavior assessment in video surveillance is approached using trajectory classification. Lagrangian state dynamic is used for probabilistic modeling of trajectory patterns and an off-line parameter learning method for the model is proposed. For classification purpose, an on-line sequential maximum a posterior trajectory classifier is introduced based on particle filter. Finally, the performance of this method is evaluated using a traffic video data set.