[1]LIU Wei,JIN Bao,ZHOU Xuan,et al.Correlation filter target tracking algorithm based on feature fusion and adaptive model updating[J].CAAI Transactions on Intelligent Systems,2020,15(4):714-721.[doi:10.11992/tis.201803036]
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Correlation filter target tracking algorithm based on feature fusion and adaptive model updating

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 4 Page number: 714-721 Column: 学术论文—机器学习 Public date: 2020-07-05
Title:
Correlation filter target tracking algorithm based on feature fusion and adaptive model updating
Author(s):
LIU Wei1; 2; 3;  JIN Bao1; 2; 3;  ZHOU Xuan1; 2; 3;  FU Jie1; 2; 3;  WANG Xinyu1; 2; 3;  GUO Zhiqing1; 2; 3;  NIU Yingjie1; 2; 3
1. School of Sciences, Liaoning Technical University, Fuxin 123000, China;
2. Institute of Intelligence Engineering and Mathematics, Liaoning Technical University, Fuxin 123000, China;
3. Institute of Mathematics and Systems Science, Liaoning Technical University, Fuxin 123000, China
Keywords:
object tracking; correlation filter; feature fusion; model updating; object occlusion; background interference; computer vision; singular value decomposition
CLC:
TP301
DOI:
10.11992/tis.201803036
Abstract:
For the problem of object tracking failure caused by background interference, object occlusion in object tracking algorithm based on a single feature, and the problem of error updating and poor real-time performance caused by model updating for each frame during tracking, a correlation filtering object tracking algorithm based on feature fusion and adaptive model updating is proposed in this paper. In the feature extraction phase, the edge feature and HOG feature are weighted together as the object features to enhance the learning of edge features. During the updating phase of the model, calculating the similarity of singular value eigenvector between the predicted region and the real region, and according to the set threshold to determine whether the model needs to be updated or not, and reducing the number of updates to the model by adaptively updating. The algorithm is verified under the standard test video set, and compared with two typical correlation filtering algorithms. The results show that the algorithm can better adapt to background interference and object occlusion problem. The average center location error is reduced by 9.05 pixels, the average distance precision is increased by 12.2%, and the average overlapping precision is increased by 4.53%.
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