Abstract: In the process of object tracking,an important way to improve the performance of the tracking algorithm is to improve the scale adaptive strategy and select features with strong discrimination ability.In order to solve the problemthat Kernel Correlation Filtering (KCF) can't adapt to the condition of object scale variation,andonlyusesthe single feature of Histogram of Oriented Gradient(HOG) whose discrimination ability to object is insufficient,a new scale adaptive strategy is proposed by studying the correlation response value of the same object at different scales,and finding the changing rule based on the analysis of a large number of statistical data,the method of linear weighted fusion of HOG and Color Name (CN) is also adopted to improves the object discrimination ability of the algorithm.Experimental results on OTB dataset show that the precision and success rate of the proposed algorithm are 8.5% and 28.9% higher than those of KCF algorithm,8.1% and 38.5% higher than those of KCF algorithm on scale variation attribute video sequence,and the performance on other attribute video sequence is also greatly improved,and the tracking speed reaches 37.68 FPS,which meets the real-time requirements.

Key words: Adaptive scale, Feature fusion, Kernel correlation filtering, Object tracking