Parallel Implementation Strategy for CoHOG-Based Pedestrian Detection Using a Multi-Core Processor

この論文にアクセスする

この論文をさがす

著者

    • MIYAMOTO Ryusuke
    • Graduate School of Information Science, Nara Institute of Science and Technology
    • SUGANO Hiroki
    • Graduate School of Information Science, Nara Institute of Science and Technology

抄録

Pedestrian detection from visual images, which is used for driver assistance or video surveillance, is a recent challenging problem. Co-occurrence histograms of oriented gradients (CoHOG) is a powerful feature descriptor for pedestrian detection and achieves the highest detection accuracy. However, its calculation cost is too large to calculate it in real-time on state-of-the-art processors. In this paper, to obtain optimal parallel implementation for an NVIDIA GPU, several kinds of parallelism of CoHOG-based detection are shown and evaluated suitability for implementation. The experimental result shows that the detection process can be performed at 16.5fps in QVGA images on NVIDIA Tesla C1060 by optimized parallel implementation. By our evaluation, it is shown that the optimal strategy of parallel implementation for an NVIDIA GPU is different from that of FPGA. We discuss about the reason and show the advantages of each device. To show the scalability and portability of GPU implementation, the same object code is executed on other NVIDA GPUs. The experimental result shows that GTX570 can perform the CoHOG-based pedestiran detection 21.3fps in QVGA images.

収録刊行物

  • IEICE transactions on fundamentals of electronics, communications and computer sciences

    IEICE transactions on fundamentals of electronics, communications and computer sciences 94(11), 2315-2322, 2011-11-01

    一般社団法人 電子情報通信学会

参考文献:  28件中 1-28件 を表示

被引用文献:  1件中 1-1件 を表示

各種コード

  • NII論文ID(NAID)
    10030191929
  • NII書誌ID(NCID)
    AA10826239
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168508
  • データ提供元
    CJP書誌  CJP引用  J-STAGE 
ページトップへ