The Fusion of Adaptive Color Attributes for Robust Compressive Tracking

In the process of target tracking, effective extraction and description of the target feature is very important, however, the existing methods mainly depend on the intensity image and ignore the color information. In this paper, an improved compressive tracking algorithm efficiently fusing adaptive color information is proposed for more robust tracking. At first, the CN color attribute is used to describe the color appearance of the target. In order to reduce the operation processing burden, the two most distinctive color components are extracted adaptively by PCA from 11-channel CN color space. In order to adapt to the scale change caused by motion, a scale-invariant normalized rectangular feature is proposed. Constant learning rate of naive Bayesian classifier parameters results in poor robustness of original compressive tracking algorithm, a novel non-linear parameter updating strategy based on double S-shape function is adopted to automatically adjust the learning rate for higher stability to interference. Finally, the scale-invariant appearance model combining the adaptive color information is integrated with particle filter frame to eliminate the negative effects such as scale change, occlusion and illumination change. Experimental results on testing sequences demonstrate the remarkable performance of our method. Compared with several well-known tracking algorithms, the average center location error is reduced to 9.88 while the suboptimal tracker is 23.9, the average overlap ratio increases by 7% points at least.