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| Multi-scale Correlation Filtering Tracking Based on Adaptive Feature Fusion |
| ZHANG Li-guo,YANG Man,ZHOU Si-en,JIN Mei |
| School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China |
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Abstract In order to reduce the influence of target deformation, illumination, motion blur and target rotation on the tracking effect in target tracking, a multi-scale correlation filtering tracking algorithm based on adaptive feature fusion is proposed on the basis of correlation filtering KCF. Firstly, the features of conv2-2, conv3-4 and conv5-4 layers and CN features in vgg19 network are extracted, and CN features are added to conv2-2 layer. Then, the three features are used to replace the hog feature for filtering learning, and three pairs of response diagrams are obtained. Then, the weighted fusion of the three response maps is carried out to predict the target position.Finally, multi-scale correlation filter is introduced to determine the scale.Compared with KCF tracking algorithm, the accuracy and success rate of this algorithm are improved by 13.6% and 11.8% respectively.Compared with several existing excellent tracking algorithms, the algorithm has better tracking effect in dealing with motion blur, background clutter, target deformation and plane rotation.
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Received: 19 March 2021
Published: 14 October 2022
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2022, Vol. 43 
