Abstract: In the visual object tracking,correlation tracking algorithm is a hot topic and has developed rapidly in recent years.Correlation filter tracking algorithms have the advantages of fast speed and good effect.However,the traditional hand-crafted features are insufficient for target discrimination,and fail to handle challenging situations such as deformation,occlusion and blurring.Recently,convolutional neural networks have achieved great success in many fields.Researchers have combined correlation tracking and convolutional features to surmount the shortcomings of hand-crafted features that lack target semantic information.In order to cope well with the above problems,this paper proposed a vi-sual object tracking algorithm based on correlation filter with hierarchical convolutional features.The proposed algorithm divides the object tracking into two steps,including position prediction and scale estimation.Multi-layer convolutional features are trained and the target position on each convolutional layer is predicted with a coarse-to-fine searching approach.The Histogram of Oriented Gradient features is used to estimate the optimal scale of target.Comprehensive experiments on 20 challenging sequences were performed to verify the proposed algorithm,and the proposed algorithm was compared with other four trackers.The results show that the proposed approach significantly improves the performance by 48.9% and 51.9% in distance precision and overlap precision respectively compared to the DSST tracker based on hand-crafted features.Moreover,the proposed method outperforms the HCFT tracker using convolutional features by 19.1% and 25.2% in distance precision and overlap precision,respectively.The proposed algorithm overcomes the shortcomings of poor representation skills of traditional manual features,and its performance is better than the correlation filtering tracking algorithms using manual features.Compared with the same correlation filtering algorithms using convolutional features,the tracking performance has also been improved.The algorithm can accurately track the target in complex situations such as occlusion and blurring.

Key words: Convolutional feature, Correlation filter, Scale estimation, Visual object tracking