Segmentation-Guided Tracking with Prior Map Decision

For visual tracking, the target object is represented by an appearance model and the location of the target is estimated in each frame. Numerous tracking algorithms model the appearance of the target with a confidence score and rarely take into account the semantic information of the target. In this paper, we propose an efficient tracking algorithm that models the appearance of the target based on semantic segmentation. The overall architecture consists of two parts: the segmentation part and the tracking part. In the segmentation part, an attention model is employed, providing spatial highlights of the candidate region of the target. In the tracking part, the tracker is constructed by an online updated convolutional neural networks to identify the target in subsequent frames, taking advantage of the segmentation information of the target from the segmentation part. To enhance the performance of this architecture, we design an incremental updated prior map taking both the segmentation signal and the tracking signal into consideration. Extensive experiments on two benchmarks including OTB-50, OTB-100, and Temple-Color, show that the proposed method outperforms other trackers.