Journal of Applied Optics, Volume. 40, Issue 6, 1067(2019)

Embedded GPU-based parallel optimization for moving objects segmentation algorithm

ZHANG Gang1...2, MA Zhenhuan1,2, LEI Tao2, CUI Yi2 and ZHANG Sanxi3 |Show fewer author(s)
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  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    In optoelectronic surveillance systems, the pixel base adaptive segmenter (PBAS) algorithm, which is widely used in moving objects segmentation, is hard to meet the requirements of real-time applications due to its calculating complication and a large amount of computing parameters. With its pixel-level parallelism, deploying PBAS on top of graphic processing unit (GPU) is promising. This paper implements real-time optimization of PBAS on embedded GPU platform-Jetson TX2, employing methods of data storage architecture, shared memory utilization and random number generation. Experimental results show that the parallel optimization method can achieve 132 fps when processing 480×320 pixel medium-wave infrared video sequences, thus meets the real-time processing need.

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    ZHANG Gang, MA Zhenhuan, LEI Tao, CUI Yi, ZHANG Sanxi. Embedded GPU-based parallel optimization for moving objects segmentation algorithm[J]. Journal of Applied Optics, 2019, 40(6): 1067

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    Paper Information

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    Received: Jun. 10, 2019

    Accepted: --

    Published Online: Feb. 11, 2020

    The Author Email:

    DOI:10.5768/jao201940.0602004

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