Hardware Accelerator for Run-Time Learning Adopted in Object Recognition with Cascade Particle Filter

• SUGANO Hiroki

  Graduate School of Informatics, Kyoto University

• OCHI Hiroyuki

  Graduate School of Informatics, Kyoto University

• NAKAMURA Yukihiro

  Research Organization of Science and Engineering, Ritsumeikan University

• MIYAMOTO Ryusuke

  Graduate School of Information Science, Nara Institute of Science and Technology

Abstract

Recently, many researchers tackle accurate object recognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, Cascade Particle Filter, is proposed to fill such demands in real environments by constructing the object model while tracking the objects. We have been investigating to implement accurate object recognition on embedded systems in real-time. In order to apply the Cascade Particle Filter to embedded applications such as surveillance, automotives, and robotics, a hardware accelerator is indispensable because of limitations in power consumption. In this paper we propose a hardware implementation of the Discrete AdaBoost algorithm that is the most computationally intensive part of the Cascade Particle Filter. To implement the proposed hardware, we use PICO Express, a high level synthesis tool provided by Synfora, for rapid prototyping. Implementation result shows that the synthesized hardware has 1, 132, 038 transistors and the die area is 2,195µm × 1,985µm under a 0.180µm library. The simulation result shows that total processing time is about 8.2 milliseconds at 65MHz operation frequency.

Journal

• IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

  IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E92-A (11), 2801-2808, 2009

  The Institute of Electronics, Information and Communication Engineers

Keywords

• Cascade Particle Filter
• hardware accelerator
• embedded systems
• real-time processing

Details

• CRID: 1390001206310072704
• NII Article ID: 10026860873
• NII Book ID: AA10826239
• DOI: 10.1587/transfun.e92.a.2801
• ISSN: 17451337
• ISSN: 09168508
• Web Site: http://www.jstage.jst.go.jp/article/transfun/E92.A/11/E92.A_11_2801/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed