Robust Multi-camera People Tracking Using Maximum Likelihood Estimation

This paper presents a new method to track multiple persons reliably using a network of smart cameras. The task of tracking multiple persons is very challenging due to targets' non-rigid nature, occlusions and environmental changes. Our proposed method estimates the positions of persons in each smart camera using a maximum likelihood estimation and all estimates are merged in a fusion center to generate the final estimates. The performance of our proposed method is evaluated on indoor video sequences in which persons are often occluded by other persons and/or furniture. The results show that our method performs well with the total average tracking error as low as 10.2 cm. We also compared performance of our system to a state-of-the-art tracking system and find that our method outperforms in terms of both total average tracking error and total number of object loss.