Multi-human tracking using part-based appearance modelling and grouping-based tracklet association for visual surveillance applications

Although much progress has been made in multi-object tracking in recent decades due to its variety of applications including visual surveillance, traffic monitoring and medical image analysis, some difficult challenges such as the variation of object appearance and partial occlusion are still going on. In this work, we propose an effective multi-human tracking system called part-based appearance modelling and grouping-based tracklet association-based multi-human tracking (PAM-GTA-MHT). The proposed appearance model based on the upper body-centered multi-view human body part model can effectively resolve the drawback caused by inter-object occlusions and low camera positions. The grouping method embedded in global tracklet association can improve discriminability among targets with similar appearances when they are located sufficiently far away from each other. Thus, there is no need to compare all possible pairs of the detected targets in the tracklet association stage and thus it has the potential to enhance the tracking speed. We quantitatively evaluated the performance of our proposed approach on four challenging publicly available datasets and achieved a significant improvement compared to the state-of-the-art methods.