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access icon openaccess High-speed visual tracking with mixed rotation invariant description

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 03/09/2015, Published 29/02/2016

A mixed rotation invariant description (MRID)-based tracking algorithm and a novel high-speed visual tracking system that implements the algorithm are proposed. 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 local binary pattern histograms of oriented gradient (LBP-HOG) feature description. The proposed tracking system contains an image sensor, a hierarchical vision processor and a two dimension of freedom actuator. 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.

Inspec keywords: image sensors; image texture; gradient methods; object tracking; computer vision; edge detection; feature extraction

Other keywords: high-speed visual tracking system; edge information rotation invariant description; two-dimensional freedom actuator; hierarchical vision processor; LBP-HOG feature description; mixed rotation invariant description; annular histogram; vision processor; image sensor; large computation issues; local binary pattern histogram of oriented gradient feature description; MRID; texture information rotation invariant description

Subjects: Image recognition; Interpolation and function approximation (numerical analysis); Computer vision and image processing techniques; Interpolation and function approximation (numerical analysis)

References

    1. 1)
      • 4. Baopu, L., Can, Y., Qi, Z., Guoqing, X.: ‘Condensation-based multi-person detection and tracking with HOG and LBP’. IEEE Int. Conf. on Information and Automation (ICIA), Hailar, China, July 2014, pp. 267–272, doi: 10.1109/ICInfA.2014.6932665.
    2. 2)
    3. 3)
    4. 4)
      • 5. Qing, C., Dickinson, P., Lawson, S., Freeman, R.: ‘Automatic nesting seabird detection based on boosted HOG-LBP descriptors’. IEEE Int. Conf. on Image Processing (ICIP), Brussels, Belgium, September 2011, pp. 3577–3580, doi: 10.1109/ICIP.2011.6116489.
    5. 5)
    6. 6)
    7. 7)
      • 3. Gu, Q., Al Noman, A., Aoyama, T., Takaki, T., Ishii, I.: ‘A fast color tracking system with automatic exposure control’. IEEE Int. Conf. on Information and Automation (ICIA), Yinchuan, China, August 2013, pp. 1302–1307, doi: 10.1109/ICInfA.2013.6720495.
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