On-line Adaption of Class-specific Codebooks for Instance Tracking

In this work, we demonstrate that an off-line trained class-specific detector can be transformed into an instance-specific detector on-the-fly. To this end, we make use of a codebook-based detector [1] that is trained on an object class. Codebooks model the spatial distribution and appearance of object parts. When matching an image against a codebook, a certain set of codebook entries is activated to cast probabilistic votes for the object. For a given object hypothesis, one can collect the entries that voted for the object. In our case, these entries can be regarded as a signature for the target of interest. Since a change of pose and appearance can lead to an activation of very different codebook entries, we learn the statistics for the target and the background over time, i.e. we learn on-line the probability of each part in the codebook belonging to the target. By taking the target-specific statistics into account for voting, the target can be distinguished from other instances in the background yielding a higher detection confidence for the target, see Fig. 1. A class-specific codebook as in [1, 2, 3, 4, 5] is trained off-line to identify any instance of the class in any image. It models the probability of the patches belonging to the object class p ( c=1|L ) and the local spatial distribution of the patches with respect to the object center p ( x|c=1,L ) . For detection, patches are sampled from an image and matched against the codebook, i.e. each patch P(y) sampled from image location y ends at a leaf L(y). The probability for an instance of the class centered at the location x is then given by