Skip to main content
SpringerLink
Log in

High-speed visual target tracking with mixed rotation invariant description and skipping searching

基于混合旋转不变描述和跳跃搜索的高速视觉目标跟踪

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

This paper proposes a novel high-speed visual target tracking system based on mixed rotation invariant description (MRID) and skipping searching method. MRID is a novel rotation invariant description of texture and edge information by annular histograms and dominant direction. It overcomes rotation variant and large computation issues in conventional LBP-HOG feature description. The skipping searching method used in tracking can remarkably decrease the computation time by avoiding repeated searching operations. The proposed tracking system contains an image sensor, a hierarchical vision processor and an actuator with 2 dimensions of freedom (DOF). The vision processor integrates processors with pixel- and row-level parallelism to speed up the tracking algorithm. Experiment results show that the proposed system can achieve over 1000-fps processing speed of the tracking algorithm under 750 × 480 resolution image.

摘要

创新点

1.本文提出的混合旋转不变描述是一种新型的旋转不变描述,它通过带主方向的环形直方图对目标的纹理和边缘信息进行描述。与传统的LBP-HOG特征相比,本文所提算法在旋转不变性和计算量上都有了很大的改善。 2.本文提出了跳跃搜索的方法,可以极大地减少跟踪过程中冗余的搜索计算,提高算法速度。 3.本文提出的算法在一款视觉处理器上成功实现,在750×480的图像分辨率上,算法速度达到1000帧/秒。

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Yilmaz A, Javed O, Shah M, et al. Object tracking: a survey. Acm Comput Surv, 2006, 38: 81–93

    Article  Google Scholar 

  2. Wu Y, Lim J, Yang M, et al. Object tracking benchmark. IEEE Trans Pattern Anal, 2015, 37: 1834–1848

    Article  Google Scholar 

  3. Ishikawa M, Ogawa K, Komuro T, et al. A CMOS vision chip with SIMD processing element array for 1 ms image processing. In: Proceedings of IEEE International Solid-State Circuits Conference, San Francisco, 1999. 206–207

    Google Scholar 

  4. Komuro T, Ishii I, Ishikawa M, et al. A digital vision chip specialized for high-speed target tracking. IEEE Trans Elec Dev, 2003, 50: 191–199

    Article  Google Scholar 

  5. Komuro T, Kagami S, Ishikawa M, et al. A dynamically reconfigurable SIMD processor for a vision chip. IEEE J Sol St Circ, 2004, 39: 265–268

    Article  Google Scholar 

  6. Yamada Y, Ishikawa M. High speed target tracking using massively parallel processing vision. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, 1993. 267–272

    Google Scholar 

  7. Miao W, Lin Q, Zhang W, et al. A programmable SIMD vision chip for real-time vision applications. IEEE J Sol St Circ, 2008, 43: 1470–1479

    Article  Google Scholar 

  8. Namiki A, Imai Y, Ishikawa M, et al. Development of a high-speed multifingered hand system and its application to catching. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, 2003. 3: 2666–2671

    Google Scholar 

  9. Nakamura Y, Kishi K, Kawakami H, et al. Heartbeat synchronization for robotic cardiac surgery. In: Proceedings of the International Conference on Robotics and Automation, Seoul, 2001. 2: 2014–2019

    Google Scholar 

  10. Nie Y, Ishii I, Yamamoto K, et al. Real-time scratching behavior quantification system for laboratory mice using high-speed vision. J Real-Time Imag Proc, 2009, 4: 181–190

    Article  Google Scholar 

  11. Wu J, Chen D, Yi R. Real-time compressive tracking with motion estimation. In: Proceedings of IEEE International Conference on Robotics and Biomimetics, Shenzhen, 2013. 2374–2379

    Google Scholar 

  12. Zdenek K, Krystian M, Jiri M, et al. Tracking-learning-detection. IEEE Trans Pattern Anal, 2011, 34: 1409–1422

    Google Scholar 

  13. Li B, Yang C, Zhang Q, et al. Condensation-based multi-person detection and tracking with HOG and LBP. In: Proceedings of IEEE International Conference on Information and Automation, Hailar, 2014. 267–272

    Google Scholar 

  14. Qing C, Dickinson P, Lawson S, et al. Automatic nesting seabird detection based on boosted HOG-LBP descriptors. In: Proceedings of IEEE International Conference on Image Processing, Brussels, 2011. 3577–3580

    Google Scholar 

  15. Ojala T, Pietikainen M, Maenpaa T, et al. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal, 2002, 24: 971–987

    Article  MATH  Google Scholar 

  16. Viola P, Jones M J. Robust Real-Time Face Detection. Int J Comput Vis, 2004, 57: 137–154

    Article  Google Scholar 

  17. Shi C, Yang J, Han Y, et al. A 1000fps vision chip based on a dynamically reconfigurable hybrid architecture comprising a PE array and self-organizing map neural network. In: Proceedings of IEEE International Solid-State Circuits Conference, San Francisco, 2014. 128–129

    Google Scholar 

  18. Yang Y, Yang J, Liu L, et al. High-speed target tracking system based on a hierarchical parallel vision processor and gray-level LBP algorithm. IEEE Trans Syst Man Cybernet: Syst, “In press”

  19. Zhang W, Fu Q, Wu N J, et al. A programmable vision chip based on multiple levels of parallel processors. IEEE J Sol St Circ, 2011, 46: 2132–2147

    Article  Google Scholar 

  20. Yang J, Shi C, Liu L, et al. Heterogeneous vision chip and LBP-based algorithm for high-speed tracking. Elect Lett, 2014, 50: 438–439

    Article  Google Scholar 

  21. Gu Q, Noman A A, Aoyama T, et al. A fast color tracking system with automatic exposure control. In: Proceedings of IEEE International Conference on Information and Automation, Yinchuan, 2013. 1302–1307

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61234003, 61434004, 61504141) and CAS Interdisciplinary Project (Grant No. KJZD-EW-L11-04).

Author information

Authors and Affiliations

  1. State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China

    Yongxing Yang, Zhongxing Zhang, Liyuan Liu & Nanjian Wu

  2. Department of Electrical and Computer Engineering, University of Calgary, Calgary, T2N1N4, Canada

    Jie Yang

Authors
  1. Yongxing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhongxing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nanjian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nanjian Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Yang, J., Zhang, Z. et al. High-speed visual target tracking with mixed rotation invariant description and skipping searching. Sci. China Inf. Sci. 60, 062401 (2017). https://doi.org/10.1007/s11432-016-0037-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-016-0037-0

Keywords

关键词

Search

Navigation