Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs

Jaehyun Song, Hwanjin Jeong, Jinkyu Jeong

    Research output: Contribution to journalArticlepeer-review


    Machine-learning-based computer vision is increasingly versatile and being leveraged by a wide range of smart devices. Due to the limited performance/energy budget of computing units in smart devices, the careful implementation of computer vision algorithms is critical. In this paper, we analyze the performance bottleneck of two well-known computer vision algorithms for object tracking: object detection and optical flow in the Open-source Computer Vision library (OpenCV). Based on our in-depth analysis of their implementation, we found the current implementation fails to utilize Open Computing Language (OpenCL) accelerators (e.g., GPUs). Based on the analysis, we propose several optimization strategies and apply them to the OpenCL implementation of object tracking algorithms. Our evaluation results demonstrate the performance of the object detection is improved by up to 86% and the performance of the optical flow by up to 10%. We believe our optimization strategies can be applied to other computer vision algorithms implemented in OpenCL.

    Original languageEnglish
    Article number7801
    JournalApplied Sciences (Switzerland)
    Issue number15
    Publication statusPublished - 2022 Aug

    Bibliographical note

    Funding Information:
    This work was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2C2102406), by the MSIT (Ministry of Science and ICT), Korea under the ICT Creative Consilience program (IITP-2020-0-01821).

    Publisher Copyright:
    © 2022 by the authors.

    All Science Journal Classification (ASJC) codes

    • Materials Science(all)
    • Instrumentation
    • Engineering(all)
    • Process Chemistry and Technology
    • Computer Science Applications
    • Fluid Flow and Transfer Processes


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