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SCIENCE CHINA Information Sciences, Volume 60, Issue 6: 062401(2017) https://doi.org/10.1007/s11432-016-0037-0

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

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  • ReceivedApr 6, 2016
  • AcceptedMay 7, 2016
  • PublishedDec 8, 2016

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 \linebreak 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 $\times$ 480 resolution image.


Funded by

CAS Interdisciplinary Project(KJZD-EW-L11-04)

the National Natural Science Foundation of China(61434004)

the National Natural Science Foundation of China(61234003)

the National Natural Science Foundation of China(61504141)


Acknowledgment

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).


References

[1] Yilmaz A, Javed O, Shah M, et al. Object tracking: a survey. Acm Comput Surv, 2006, 38: 81-93 Google Scholar

[2] Wu Y, Lim J, Yang M, et al. Object tracking benchmark. IEEE Trans Pattern Anal, 2015, 37: 1834-1848 CrossRef 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 CrossRef 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 CrossRef 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 CrossRef 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 CrossRef 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 CrossRef Google Scholar

[16] Viola P, Jones M J. Robust Real-Time Face Detection. Int J Comput Vis, 2004, 57: 137-154 CrossRef 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". Google Scholar

[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 CrossRef 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 CrossRef 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

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