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Memory access minimization for mean-shift tracking in mobile devices

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Abstract

Due to the development of artificial intelligence and computer vision technology, many autonomous drones have been studied. However, computer vision technology requires high performance CPU due to its high complexity, and battery consumption is so high that drones are constrained to fly for a long time. Therefore, low-power mobile devices require tracking algorithms that minimize battery consumption. In this paper, we propose a mean-shift based tracking algorithm that minimizes memory access to reduce battery consumption. To accomplish this, we minimize the number of memory accesses by using an algorithm that divides the direction of the mean-shift vector into eight, and calculates the sum of the density maps only for the new area without calculating the sum of the density maps for the already calculated area. It is possible to increase the calculation efficiency by lowering the memory access cost. Experimental results show that the proposed method is more efficient than the existing method.

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Acknowledgements

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(Ministry of Science, ICT & Future Planning) (No. NRF-2017R1C1B5017751)

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Authors and Affiliations

  1. Division of Software Application, Kangnam University, Yongin-si, South Korea

    Kwontaeg Choi

  2. Department of Computer Science, Gyeongsang National University, Jinju, South Korea

    Beom-Seok Oh

  3. Department of Culture Techno, Changwon National University, Changwon, South Korea

    Sunjin Yu

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  1. Kwontaeg Choi
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  2. Beom-Seok Oh
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  3. Sunjin Yu
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Correspondence to Sunjin Yu.

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Choi, K., Oh, BS. & Yu, S. Memory access minimization for mean-shift tracking in mobile devices. Multimed Tools Appl 80, 34173–34187 (2021). https://doi.org/10.1007/s11042-020-09364-w

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  • DOI: https://doi.org/10.1007/s11042-020-09364-w

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