Improved Target Tracking Algorithm of Kernel Correlation Filtering Based on Deep Learning
LIANG Huagang
GAO Dongmei
PANG Liqin
Abstract：In order to solve the problem that the tracking accuracy of the traditional target tracking algorithm is insufficient, this paper uses the convolution neural network to extract the image depth features and overcome the robustness of the traditional fea?tures. Secondly,combined with the improved target tracking algorithm of nuclear correlation filtering,when the target is blocked, the target can be tracked accurately. In this paper,representative video is selected to test the algorithm and compared with tradition?al features. The results show that the accuracy of the algorithm is increased by 17.6 and the tracking rate reaches 20.59fps.
Keywords：convolution neural networkfeature extractioncorrelation filteringtarget tracking
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1115-1119 )