Toward real-time pedestrian detection based on a deformable template model

Marco Pedersoli, Jordi Gonzalez, Xu Hu, Xavier Roca

Research output: Contribution to journalArticleResearchpeer-review

33 Citations (Scopus)

Abstract

Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed. © 2013 IEEE.
Original languageEnglish
Article number6609099
Pages (from-to)355-364
JournalIEEE Transactions on Intelligent Transportation Systems
Volume15
Issue number1
DOIs
Publication statusPublished - 1 Feb 2014

Keywords

  • Driving assistance
  • object detection
  • pattern recognition

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