Junseok Kwon , Seoul National University, Seoul
Kyoung Mu Lee , Seoul National University, Seoul
We propose the visual tracker sampler, a novel tracking algorithm that can work robustly in a challenging scenario, such that several kinds of appearance and motion changes of an object can occur simultaneously. The proposed tracking algorithm tracks a target by searching for appropriate trackers in each frame. Since the real-world tracking environment varies severely over time, the trackers should be adapted or newly constructed depending on the current situation, so that each specific tracker takes charge of a certain change in the object. To do this, our 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 (MCMC) method from the predefined tracker space by proposing new appearance models, motion models, state representation types, and observation types, which are the important ingredients of visual trackers. All trackers are then integrated into one compound tracker through an Interacting MCMC (IMCMC) method. By exchanging information with others through IMCMC, each tracker further improves its performance, thus increasing overall tracking performance. Experimental results show that our method tracks the object accurately and reliably in realistic videos, where ppearance and motion drastically change over time, and outperforms even state-of-the-art tracking methods.
Tracking, Computer vision
J. Kwon and K. M. Lee, "Tracking by Sampling and Integrating Multiple Trackers," in IEEE Transactions on Pattern Analysis & Machine Intelligence.