Object tracking by features and particles selection in multiple subspace

The existing appearance models and motions researches for object tracking are usually restricted to a single space, and thus the appearance models are not abundant enough for the object representation. In addition, the particles' accuracies are not good enough to provide the candidate object positions after particle filtration. Herein, we propose an object tracking algorithm based on selection of features and particles in multiple subspaces. First of all, discriminative subspace matrix of the object was built according to discriminative subspaces. To get the discriminative features of the subspaces, PCA was proposed. Thus, discriminative subspace and colour matrix for the object was successfully represented. Furthermore, in motion researching, the particles are selected based on the features' similarity and the spatial coherence in multiple discriminative subspaces. Based on the appearance model and those selected effective particles, the algorithm determines the final position. Experimental results show that the proposed algorithm outperforms the state-of-the-art schemes, especially for the illumination changes, rotations, scaling and fast motion.