Robust object tracking with scene-adaptive scheme in occlusion

Abstract

Occlusion handling is a challenging problem in object tracking. Most existing methods fail to handle well in complex image sequences. This paper presents a scene adaptive tracking algorithm in occlusion. We decompose the tracking into target translation and scale prediction. A kernelized correlation filter with an adaptive update scheme is adopted to estimate target position. The adaptive online update scheme takes advantage of the confidence score sensitivity to occlusion and reduces the false updating in occlusion during the tracking sequence. The target scale can be estimated by the correlation filter with the ridge regression. Extensive experiments results on 29 challenging occlusion sequences show that the proposed tracking approach achieves the average overlap precision (OP) of 72.2%, which improves the performance by 7.6% compared to the DSST. On OTB-50 dataset, our tracking approach is also superior comparing to several state-of-the-art trackers.