Object Tracking with Classification Score Weighted Histogram of Sparse Codes

Object tracking involves target localization in dynamic scenes using either generative models, discriminative classifiers or their combination. We propose a combined approach consisting of generative models (learned in sparse representation framework) and discriminative classifiers (SVM). Sparse codes are initially computed from two different dictionaries constructed from foreground and background patches using K-SVD. SVM learned on these sparse codes provides classifier scores for patches. These scores for sparse codes of patches drawn from a region are used to form a weighted histogram. This weighted histogram of sparse codes form the object and candidate models. The learned dictionaries provide distinct representations for object and background patches. This discrimination is further enhanced by classifier scores. The object is localized by maximizing Bhattacharyya coefficient between target and candidate models in a particle filter framework. Performance of the proposed tracker is benchmarked on videos from VOT2014 dataset against existing generative and discriminative approaches. Our proposal was able to handle different challenging situations involving background clutter, in-plane rotations, scale and illumination changes.