Weighted Correlation Filter Tracking Algorithm Based on Context and Relocation

被引：0
|
作者：
Xiong C. [1 ]
Lu Y. [1 ]
Yan J. [1 ]
机构：
[1] Beijing Key Laboratory of Urban Intelligent Control Technologies, North China University of Technology, Beijing
来源：
Guangxue Xuebao/Acta Optica Sinica | 2019年 / 39卷 / 04期
关键词：
Adaptive iteration; Contextual features; Correlation filtering; Machine vision; Relocation; Weighted fusion;
D O I：
10.3788/AOS201939.0415004
中图分类号：
学科分类号：
摘要：
In order to improve both the tracking accuracy and speed of the efficient convolution operators based tracking algorithm fusing the histogram of oriented gradient and color names features (ECO-HC), a weighted correlation filtering algorithm based on context and relocation is proposed. Considering the differences between the histogram of oriented gradient and color names features, the responses of two features are fused in different weights. The adaptive iterative method is used to predict the position of a target, which combines with the multi-scale search area, the contextual features and the relocation method when the target prediction is failure to further improve the tracking accuracy. The algorithm is evaluated on the OTB-100 dataset. The experimental results show that the average distance accuracy of the proposed algorithm is 89.2% and the average overlap rate is 80.6%, 3.6% and 2.1% higher than those of the ECO-HC method, respectively. In addition, the tracking speed on the central processing unit is 65.2 frame/s, superior to that of the other tracking algorithms compared in the experiments. The proposed algorithm effectively improves the tracking accuracy and can track the objects well under the condition of severe occlusion, illumination variation and other interferences. © 2019, Chinese Lasers Press. All right reserved.
引用
收藏
相关论文
共 21 条
  • [11] Danelljan M., Hager G., Khan F.S., Et al., Learning spatially regularized correlation filters for visual tracking, IEEE International Conference on Computer Vision, pp. 4310-4318, (2015)
  • [12] Galoogahi H.K., Fagg A., Lucey S., Learning background-aware correlation filters for visual tracking, IEEE International Conference on Computer Vision, pp. 1144-1152, (2017)
  • [13] Wang M.M., Liu Y., Huang Z.Y., Large margin object tracking with circulant feature maps, IEEE Conference on Computer Vision and Pattern Recognition, pp. 4800-4808, (2017)
  • [14] Danelljan M., Hager G., Khan F.S., Et al., Discriminative scale space tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 8, pp. 1561-1575, (2017)
  • [15] Li Y., Zhu J.K., A scale adaptive kernel correlation filter tracker with feature integration, European Conference on Computer Vision, pp. 254-265, (2014)
  • [16] Danelljan M., Bhat G., Khan F.S., Et al., ECO: efficient convolution operators for tracking, IEEE Conference on Computer Vision and Pattern Recognition, pp. 6931-6939, (2017)
  • [17] Xiong C.Z., Che M.Q., Wang R.L., Et al., Robust real-time visual tracking via dual model adaptive switching, Acta Optica Sinica, 38, 10, (2018)
  • [18] Wu Y., Lim J., Yang M.H., Object tracking benchmark, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 9, pp. 1834-1848, (2015)
  • [19] Liang P.P., Blasch E., Ling H.B., Encoding color information for visual tracking: algorithms and benchmark, IEEE Transactions on Image Processing, 24, 12, pp. 5630-5644, (2015)
  • [20] Lukezic A., Vojir T., Zajc L.C., Et al., Discriminative correlation filter with channel and spatial reliability, IEEE Conference on Computer Vision and Pattern Recognition, pp. 4847-4856, (2017)
123