Visual object tracking with discriminative correlation filtering and hybrid color feature

The technology of visual object tracking based on correlation filter has good accuracy and efficiency. However, it is still necessary to be study further on the appearance model of the target, the scale variation of the target and so on. This paper proposes a tracking algorithm based on discriminative correlation filtering and a hybrid color feature. The hybrid color feature is composed of two parts, which are compressed color name features and Histogram of Oriented Gradient features based on opponent color space. These two parts features above are extracted from the target patch, respectively. For the first part, color-name features are extracted from a target patch firstly, and then block-based compressed color-name features are extracted according to these color-name features. For the second part, opponent color features are extracted from the target patch firstly, and then HOG features are extracted from these opponent color features. At the basis of the hybrid color feature, two different discriminative correlation filters are used to estimate the translation and the scale of the target, respectively. Finally, extensive experiments show that the tracking algorithm with the hybrid color features of this paper outperforming several state-of-the-art tracking algorithms.