Accelerated Deformable Part Models on GPUs

Manato Hirabayashi, Shinpei Kato, Masato Edahiro, Kazuya Takeda, Seiichi Mita

研究成果: Article査読

5 被引用数 (Scopus)

抄録

Object detection is a fundamental challenge facing intelligent applications. Image processing is a promising approach to this end, but its computational cost is often a significant problem. This paper presents schemes for accelerating the deformable part models (DPM) on graphics processing units (GPUs). DPM is a well-known algorithm for image-based object detection, and it achieves high detection rates at the expense of computational cost. GPUs are massively parallel compute devices designed to accelerate data-parallel compute-intensive workload. According to an analysis of execution times, approximately 98 percent of DPM code exhibits loop processing, which means that DPM could be highly parallelized by GPUs. In this paper, we implement DPM on the GPU by exploiting multiple parallelization schemes. Results of an experimental evaluation of this GPU-accelerated DPM implementation demonstrate that the best scheme of GPU implementations using an NVIDIA GPU achieves a speed up of 8.6x over a naive CPU-based implementation.

本文言語English
論文番号7152943
ページ(範囲)1589-1602
ページ数14
ジャーナルIEEE Transactions on Parallel and Distributed Systems
27
6
DOI
出版ステータスPublished - 2016 6 1
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • ハードウェアとアーキテクチャ
  • 計算理論と計算数学

フィンガープリント

「Accelerated Deformable Part Models on GPUs」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル