Multiple People Tracking in Smart Camera Networks by Greedy Joint-Likelihood Maximization

This paper presents a new method to track multiple people reliably using a network of calibrated smart cameras. The task of tracking multiple persons is very difficult due to non-rigid nature of the human body, occlusions and environmental changes. Our proposed method recursively updates the positions of all persons based on the observed foreground images from all smart cameras and the previously known location of each person. The performance of our proposed method is evaluated on indoor video sequences containing person– person/object–person occlusions and sudden illumination changes. The results show that our method performs well with Multiple Object Tracking Accuracy as high as 100% and Multiple Object Tracking Precision as high as 86%. Performance comparison to a state of the art tracking system shows that our method outperforms.