Towards Occlusion Handling: Object Tracking With Background Estimation

Abstract

The appearance model of the target needs to be updated for online single object tracking. However, the variation of the observation can be caused by active appearance change of the target, or the occlusion from the background. For the former case, we should update the appearance model and for the latter, the current model should be preserved. In this paper, we distinguish these two cases and resist the impact from heavy occlusion by estimating the background in the scene with moving cameras, while retaining the adaptivity to stationary cameras at the same time. The proposed method formulates the background as a Gaussian model and the target is determined in a coarse-to-fine manner. Experimental results demonstrate that our method achieves competitive results in the sequences with appearance changes and outperforms the state-of-the-art algorithms in dealing with complex occlusions.