Robust visual tracking based on watershed regions

Robust visual tracking is a very challenging problem especially when the target undergoes large appearance variation. In this study, the authors propose an efficient and effective tracker based on watershed regions. As middle-level visual cues, watershed regions contain more semantics information than low-level features, and reflect more structure information than high-level model. First, the authors manually select the target template in initial frame, and predict the target candidate in the next frame using motion prediction. Then, the authors utilise marker-based watershed algorithm to obtain the watershed regions of target template and candidate template, and describe each region with multiple features. Next, the authors calculate the nearest neighbour in feature space to match the watershed regions and construct an affine relation from target template to candidate template. Finally, the authors resolve the affine relation to calculate the final tracking result, and update the template for the following tracking. The authors test their tracker on some challenging sequences with appearance variation range from illumination change, partial occlusion, pose change to background clutters and compare it with some state-of-the-art works. Experiment results indicate that the proposed tracker is robust to the large appearance variation and exceeds the state-of-the-art trackers in most situations.