To reduce the influence of factors such as illumination variation, scale variation, partial occlusion in target tracking, a target tracking algorithm based on Kernelized Correlation Filter (KCF) with block-based model was proposed. Firstly, the feature of histogram of oriented gradients and the feature of color name were combined to better characterize the target. Secondly, the method of scale pyramid was adopted to estimate the target scale. Finally, the peak to sidelobe ratio of the feature response map was used to detect occlusion, and the partial occlusion problem was solved by introducing a high-confidence block relocation module and a dynamic strategy for model adaptive updating. To verify the effectiveness of the proposed algorithm, comparative experiments with several mainstream algorithms on various datasets were conducted. Experimental results show that the proposed algorithm has the highest precision and success rate which are respectively 11.89% and 15.24% higher than those of KCF algorithm, indicating that the proposed algorithm has stronger robustness in dealing with factors like illumination variation, scale variation and partial occlusion.