Adaptive weighting of local classifiers by particle filters for robust tracking

摘要

This paper presents an adaptive weighting method for combining local classifiers using a particle filter. Although the effectiveness of weighting methods based on combinations of local classifiers (features) has been reported recently, such methods fail in cases where there is partial occlusion or when shadows appear due to changes in the illumination direction since fixed weights are used for combining the local classifiers. In order to achieve the desired robustness, the weights should be changed adaptively. For this purpose, we use a particle filter, where each particle is assigned to the weight set for combining local classifiers. By estimating the posterior probability in weight space by using a particle filter, the effective weights for current time-step are obtained, and as a result the proposed method can account for dynamic occlusion. As a means of a demonstration, our approach is applied to the face tracking problem. The adaptability and the robustness of the method with respect to partial occlusion are evaluated using test sequences in which the occluded areas are changed dynamically. The weights of the occluded regions decrease automatically without the need for explicit knowledge about the occurrence of occlusion, which makes it possible to track the face under conditions of dynamic occlusion.