Best-Buddies Tracking
This work addresses tracking challenges in computer vision, but it is incremental as it modifies an existing similarity measure for improved performance.
The paper tackled model-free online tracking by adapting the Best-Buddies Similarity (BBS) measure to handle point sets of different sizes, enabling better handling of scale changes and variable template images, and achieved good results on standard benchmarks.
Best-Buddies Tracking (BBT) applies the Best-Buddies Similarity measure (BBS) to the problem of model-free online tracking. BBS was introduced as a similarity measure between two point sets and was shown to be very effective for template matching. Originally, BBS was designed to work with point sets of equal size, and we propose a modification that lets it handle point sets of different size. The modified BBS is better suited to handle scale changes in the template size, as well as support a variable number of template images. We embed the modified BBS in a particle filter framework and obtain good results on a number of standard benchmarks.