An Improved C-COT Based Visual Tracking Scheme to Weighted Fusion of Diverse Features.

Visual tracking is one of hot researches in computer vision in recent years. C-COT [8] has obtained excellent results on many visual tracking benchmarks. However, it cannot exploit CNN features effectively because it gave the same weight for different CNN features. Furthermore, it updated model frame by frame, it possibly results in model drift. To address these problems, we propose an improved C-COT based visual tracking scheme to weighted fusion of diverse features. We present a weighted sum model that convolutional responses from different convolutional layers are weighted and summed to obtain the final response score. Secondly, we introduce a context based updating strategy for high confidence model update to avoid samples corruption and model drift. The experimental results on the challenging OTB dataset demonstrate that the proposed method is more competitive than state-of-the-art trackers.