Salient Region-Based Online Object Tracking

In this paper, we propose a salient region-based tracking method that discriminates the exact target region from background by using a probabilistic color model. The color model is updated using image pixels included in salient region. From the extracted salient region, we derive shape model which can be combined with color model that enable the tracker to be robust when the color distribution of target object is similar with other objects. Additionally, we adopt template matching weighted by the shape model to discriminate the target when the background has very similar color distribution with target object. The weight between color matching and template matching is automatically determined based on the confidence of the response map. The proposed method is robust to scale change, object transformation, and rotation. In experiments on public datasets, the proposed method achieved a higher result compared with existing state-of-the-art methods in terms of Expected Overlap Ratio (EAO) only using color model and template matching. The internal analysis proves that the combination of salient region and shape model can increase the tracking performance.