A New Fisheye Video Target Tracking Method by Integrating Response Template and Multiple Features

被引：0
作者：
Zhou X. [1 ,2 ]
Huang C. [2 ]
Shao Z. [2 ]
Chen S. [2 ,3 ]
Lei B. [4 ]
机构：
[1] College of Electrical and Information Engineering, Quzhou University, Quzhou
[2] College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou
[3] School of Computer Communication and Engineering, Tianjin University of Technology, Tianjin
[4] Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 07期
关键词：
Correlation filter; Fisheye video; Image distortion; Target tracking;
D O I：
10.3724/SP.J.1089.2019.17442
中图分类号：
TN911 [通信理论];
学科分类号：
081002 ;
摘要：
The wide use of fisheye cameras makes target tracking on fisheye video get increasing attention. However, the serious distortion caused by the special imaging principle of the fisheye lens brings the target tracking negative effect. Aiming at weakening the interference of the distortion, this paper proposes a novel fisheye video target tracking method based on response template and feature integration. Firstly, the proposed method synthesizes the response template based on the responses of multiple samples as well as constructs a classifier based on the response template, and then extracts the object's HoG feature and Color Name feature respectively to train the corresponding classifiers. The responses of two classifiers are considered jointly to determine the target location. For further optimizing the tracker, imaging model is used to correct the deformed target before the training of the classifier. Finally, the evaluation results on the constructed fisheye video dataset validate that the proposed method can greatly reduce the negative impact of the image distortion and the target deformation while keeping the real-time performance. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
引用
收藏
页码：1067 / 1074
页数：7
相关论文
共 25 条
  • [11] Henriques J.F., Caseiro R., Martins P., Et al., High-speed tracking with kernelized correlation filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 3, pp. 583-596, (2015)
  • [12] Danelljan M., Shahbaz Khan F., Felsberg M., Et al., Adaptive color attributes for real-time visual tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090-1097, (2014)
  • [13] Montero A.S., Lang J., Laganiere R., Scalable kernel correlation filter with sparse feature integration, Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 587-594, (2015)
  • [14] Bertinetto L., Valmadre J., Golodetz S., Et al., Staple: complementary learners for real-time tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1401-1409, (2016)
  • [15] Danelljan M., Hager G., Shahbaz Khan F., Et al., Convolutional features for correlation filter based visual tracking, Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 621-629, (2015)
  • [16] Danelljan M., Robinson A., Shahbaz Khan F., Et al., Beyond correlation filters: Learning continuous convolution operators for visual tracking, Proceedings of European Conference on Computer Vision, pp. 472-488, (2016)
  • [17] Danelljan M., Bhat G., Shahbaz Khan F., Et al., ECO: efficient convolution operators for tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6931-6939, (2017)
  • [18] Mueller M., Smith N., Ghanem B., Context-aware correlation filter tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1387-1395, (2017)
  • [19] Zhang T.Z., Xu C.H., Yang M.H., Learning multi-task correlation particle filters for visual tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 2, pp. 365-378, (2019)
  • [20] Valmadre J., Bertinetto L., Henriques J., Et al., End-to-end representation learning for Correlation Filter based tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5000-5008, (2017)
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