Correlation Filter for Object Tracking Method Based on Spare Representation.

Aiming at the problem that the object tracking methods based on correlation filter is easily affected by the distractive features in complex scenes such as object deformation and background interference, which leads to the tracking failure, a correlation filter for object tracking method based on sparse representation is proposed. The method combines correlation filter with sparse representation by using L1 norm to sparse constrain the correlation filter in the objective function, so that the trained correlation filter only contains the key features of the object. At the same time, different penalty parameters are assigned to the correlation filter coefficients according to spatial position of the correlation filter coefficients, and the alternating direction method of multipliers (ADMM) is used to solve the correlation filter. The experimental results show that: the method has the best precision and success rate in comparison with five object tracking methods based on correlation filter on three commonly used datasets. At the same time, the method has good robustness to the distractive features in complex scenes, and can meet the real-time requirements. [ABSTRACT FROM AUTHOR]