Improved Object Tracking Throughout Occlusions

Abstract

Occlusions present a significant challenge to successfully track objects in video content, even with state of the art tracking algorithms. In this paper, a new tracking system, which utilizes the additional information provided by 3D video content, is presented. The system incorporates a 3D Kalman Filter coupled with occlusion reasoning, based on segmentation of the depth map, to accurately track an object during an occlusion and after it reemerges. Results show an improvement in robustness, over the state of art, especially in videos where the tracked object’s motion is linear.