Off-line trained class-specific object detectors are designed to detect any instance of the class in a given image or video sequence. In the context of object tracking, however, one seeks the location and scale of a target object, which is a specific instance of the class. Hence, the target needs to be separated not only from the background but also from other instances in the video sequence. We address this problem by adapting a class-specific object detector to the target, making it more instance-specific. To this end, we learn off-line a codebook for the object class that models the spatial distribution and appearance of object parts. For tracking, the codebook is coupled with a particle filter. While the posterior probability of the location and scale of the target is used to learn on-line the probability of each part in the codebook belonging to the target, the probabilistic votes for the object cast by the codebook entries are used to model the likelihood.