Human detection and tracking using apparent features under multi-cameras with non-overlapping

This paper describes a human detection and tracking system under multi-cameras with non-overlapping views using apparent features only. Our system is able to first detect people and then perform object matching. In the distributed intelligent surveillance system, computers need to detect pedestrians automatically under multi-cameras probably with non-overlapping views for providing a steady and continuous tracking of the pedestrian targets. In this paper, we combine Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) to detect human and segment human body from the background using GrabCut algorithm. We also study the method of pedestrian feature extraction and object matching based on appearance. We connect all the modules above in series to obtain a complete system and test it on samples we collect over three cameras with non-overlapping views to prove the effectiveness. We believe that our system will be helpful to the development of the public security system.