Unsupervised multi-target trajectory detection, learning and analysis in complicated environments

Trajectory analysis is very important to human behavior-analysis for video processing based smart surveillance systems. It has a challenge that human trajectory has no prior model and needs to online learning and updating, while interaction between targets complicates the problem. This paper describes a novel integrated framework for multiple human trajectory detection, learning and analysis in complicated environments. First a modified feature-spatial representation (MFSR) for Cam-Shift tracking algorithm is proposed to obtain trajectories. Then, a piecewise multilevel learning method is adopted to learn the trajectory patterns by using spectral clustering and Hidden Markov Model. Finally a cascade detector is established for anomaly analysis based on learning information, which allows obviously abnormal trajectories to be quickly deviated from normality. Our framework is demonstrated good results by lots of experiments and can be applied in further selective video analysis.