Learning Collaborative Sparse Correlation Filter for Real-Time Multispectral Object Tracking

To track objects efficiently and effectively in adverse illumination conditions even in dark environment, this paper presents a novel multispectral approach to deploy the intra- and inter-spectral information in the correlation filter tracking framework. Motivated by brain inspired visual cognitive systems, our approach learns the collaborative sparse correlation filters using color and thermal sources from two aspects. First, it pursues a sparse correlation filter for each spectrum. By inheriting from the advantages of the sparse representation, our filers are robust to noises. Second, it exploits the complementary benefits from two modalities to enhance each other. In particular, we take their interdependence into account for deriving the correlation filters jointly, and formulate it as a l(2,1)-based sparse learning problem. Extensive experiments on large-scale benchmark datasets suggest that our approach performs favorably against the state-of-the-arts in terms of accuracy while achieves in real-time frame rate.