Motion tracking based on area and level set weighted centroid shifting

In this study, the authors propose a stable colour-based tracking algorithm based on a new representation of the target location: the area weighted mean of the centroids corresponding to each colour bin of the target. The target location is well discriminated, since the centroids contain spatial information on the distribution of the colours and are rather insensitive to the loss of pixels and change in the number of pixels. The area weighting takes care that the major colours are treated with more importance than the minor colours. Due to these properties, it is possible to track the target in difficult conditions such as low-frame-rate environment, severe partial occlusion and partial colour change environment. Furthermore, the target localisation can be achieved in a one-step computation, which makes the algorithm fast. The authors compare the stability of the proposed tracking scheme with the original mean shift based tracker, both mathematically and experimentally. They also propose a background feature elimination algorithm, which is based on the level set based bimodal segmentation. The level set based bimodal segmentation segments out the region with dominant background feature and thus increases the robustness of the scheme.