An object tracking algorithm combining spatial information and sparse dictionary optimization

Aimming at the problem of tracking drift caused by object appearance change in complex scene, a novel object tracking algorithm based on sparse representation is proposed. An optimized objective cost function is designed with the sparsity and spatial correlation regularization constraint. The Lagrange dual theory and the accelerate proximal gradient approach are used to complete the dictionary optimization. By using the maximum pooling theory and the spatial pyramid method, the coefficients of the object template and candidate samples with the reduced dimension and more spatial information are obtained. Experimental results show that the proposed algorithm can perform robust tracking effect in a variety of complex scene, such as background clutters, illumination variation, deformation, motion blur, heavy occlusion, and so on.