Abstract

In this paper, we propose a new particle-filter based track- ing algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dic- tionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive prop- erties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumina- tion, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time com- plexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of chal- lenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also sig- nificantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1].