Real-time object tracking combined RGB-D with MeanShift

被引：0
|
作者：
Wang G. [1 ]
Tian J. [1 ]
Zhu W. [1 ]
Fang D. [1 ]
机构：
[1] Laboratory of Nanotechnology and Microsystems, Shijiazhuang Campus of Army Engineering University, Shijiazhuang
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2019年 / 50卷 / 09期
关键词：
MeanShift; Object tracking; Real-time tracking; RGB-D;
D O I：
10.11817/j.issn.1672-7207.2019.09.013
中图分类号：
学科分类号：
摘要：
Arm to solve the problem that the object is susceptible to interference from similar surrounding background in MeanShift, RGB-D feature was introduced, and thus a real-time object tracking combined RGB-D with MeanShift was proposed. Firstly, a novel similarity measurement method, advanced quadratic-form distance (AQFD),was put forward. Secondly, the online self-adaptive adjustment mechanism of feature weights and updating strategy of object model were improved. Finally, the method was simulated based on Matlab and implemented on Jetson TX2. The results show that the proposed method which is better than Staple performs well in accuracy and robustness in the case that the object is disturbed by similar surrounding background, and it can be applied to real-time tracking applications. © 2019, Central South University Press. All right reserved.
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收藏
页码：2163 / 2170
页数：7
相关论文
共 19 条
  • [1] Yin H., Chen B., Chai Y., Et al., Vision-based object detection and tracking: a review, Acta Automatica Sinica, 42, 10, pp. 1466-1489, (2016)
  • [2] Henriques J.F., Caseiro R., Martins P., Et al., Exploiting the circulant structure of tracking-by-detection with kernels, European Conference on Computer Vision, pp. 702-715, (2012)
  • [3] 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)
  • [4] Bertinetto L., Valmadre J., Golodetz S., Et al., Staple: complementary learners for real-time tracking, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1401-1409, (2016)
  • [5] Danelljan M., Khan F.S., Felsberg M., Et al., Adaptive color attributes for real-time visual tracking, Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090-1097, (2014)
  • [6] 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)
  • [7] Danelljan M., Robinson A., Khan F.S., Et al., Beyond correlation filters: learning continuous convolution operators for visual tracking, European Conference on Computer Vision, pp. 472-488, (2016)
  • [8] Danelljan M., Bhat G., Khan F.S., Et al., ECO: efficient convolution operators for tracking, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 6931-6939, (2017)
  • [9] Wang N.Y., Yeung D.Y., Learning a deep compact image representation for visual tracking, Proceedings of the 26th International Conference on Neural Information Processing Systems, pp. 809-817, (2013)
  • [10] Nam H., Han B., Learning Multi-domain convolutional neural networks for visual tracking, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4293-4302, (2015)
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