Accelerated Deformable Part Models on GPUs

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

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number7152943
Pages (from-to)1589-1602
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Volume27
Issue number6
DOIs
Publication statusPublished - 2016 Jun 1
Externally publishedYes

Keywords

  • Deformable Part Models (DPM)
  • Graphics Processing Unit (GPU)
  • Image Processing

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

Fingerprint Dive into the research topics of 'Accelerated Deformable Part Models on GPUs'. Together they form a unique fingerprint.

Cite this