Transductive inference for color-based particle filter tracking

Robust real−time tracking of non−rigid objects in a dynamic environment is a challenging task. Among various cues in tracking, color can provide an efficient visual cue for this type of tracking problem because of its invariance in the presence of changing complex shapes and appearances. To track the color object, a particle filter uses several hypotheses simultaneously and weights them by their similarity with the object color model. The use of particle filter allows better handling of color clutter and maintains track even through momentary occlusions. However, one of the major difficulties of color−based tracking is that the color changes in dynamic lighting conditions. Different from other color model adaptation methods, we propose an adaptive color−based particle filter tracking algorithm by transductive inference. The particles are given different weights as weighted unlabeled data by a weak classifier. Combining confidently labeled data and weighted unlabeled data, the proposed transductive particle filter algorithm offers an effective way to transduce object color model through the given observation in non−stationary color distributions. Experiments show that the transductive particle filter algorithm can effectively update the object color model and maintain object tracking even under temporal occlusion and color clutter.