A kernel correlation tracking algorithm exhibiting feature-weight and scale adaptation is proposed. The histogram of oriented gradient (HOG) and the color name (CN) features of the target search area are extracted for performing adaptive weight fusion, and the target position is estimated using the peak value of the correlation filter response map of the fusion feature. Further, using the product of the peak value of the correlation filter response map and the peak sidelobe ratio of the large weighted feature as the basis for scale estimation, the rough and accurate estimations of the target scale are performed and utilized to obtain the optimal scale of the target. The results of the simulation experiments performed using the object tracking benchmark (OTB-2013) dataset show that the proposed algorithm exhibits obvious improvements in terms of tracking precision and success rate compared with other five tracking algorithms. The tracking precision and success rate obtained using the proposed algorithm are 0.799 and 0.723, respectively. Furthermore, the proposed algorithm can well adapt to the change of target scale.