Real-time multi-people tracking by greedy likelihood maximization

Abstract

Unlike tracking rigid targets, the task of tracking multiple people is very challenging because the appearance and the shape of a person varies depending on the target's location and orientation. This paper presents a new approach to track multiple people with high accuracy using a calibrated monocular camera. Our approach recursively updates the positions of all persons based on the observed foreground image and previously known location of each person. This is done by maximizing the likelihood of observing the foreground image given the positions of all persons. Since the computational complexity of our approach is low, it is possible to run in real time on smart cameras. When a network of multiple smart cameras overseeing the scene is available, local position estimates from smart cameras can be fused to produced more accurate joint position estimates. The performance evaluation of our approach on very challenging video sequences from public datasets shows that our tracker achieves high accuracy. When comparing to other state-of-the-art tracking systems, our method outperforms in terms of Multiple Object Tracking Accuracy (MOTA).