Visual Tracker Sampler

A tracking framework called visual tracker sampler that tracks a target robustly by searching for the appropriate trackers in each frame is proposed by J. Kwon and K. M. Lee. Since the real world tracking environment varies severely over time, the trackers should be adapted or newly constructed depending on the current situation. To do this, the method obtains several samples of not only the states of the target but also the trackers themselves during the sampling process. The trackers are efficiently sampled using the Markov Chain Monte Carlo method from the predefined tracker space by proposing new appearance models, motion models, state representation types, and observation types, which are the basic important components of visual trackers. Then, the sampled trackers run in parallel and interact with each other while covering various target variations efficiently.

Related publications:

1. Junseok Kwon, Kyoung Mu Lee. Tracking by Sampling Trackers, IEEE International Conference on Computer Vision (ICCV) 2011

2. Junseok Kwon, Kyoung Mu Lee. Visual Tracking Decomposition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2010