Near-Infrared-Based Nighttime Pedestrian Detection Using Grouped Part Models

Yi Shu Lee, Yi Ming Chan, Li Chen Fu, Pei Yung Hsiao*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

30 Scopus citations

Abstract

Pedestrian detection is an important issue in the field of intelligent transportation systems. As a pedestrian is not an apparent object at nighttime, it brings about critical difficulties in effectively detecting a pedestrian for a driving assistant vision system. While using an infrared projector to enhance the illumination contrast, objects in a nighttime environment might reflect the infrared projected by the emitted spotlight. In some cases, however, the clothes on a pedestrian might absorb most of the infrared, thus causing the pedestrian to be partially invisible. To deal with this problem, a nighttime part-based pedestrian detection method is proposed. It divides a pedestrian into parts for a moving vehicle with a camera and a near-infrared lighting projector. Due to a high computation load, selecting effective parts becomes imperative. By analyzing the spatial relationship between every pair of parts, the confidence of the detected parts can be enhanced even when some parts are occluded. At the last stage of this system, the pedestrian detection result is refined by a block-based segmentation method. The system is verified by experiments, and the appealing results are demonstrated.

Original languageEnglish
Article number7039272
Pages (from-to)1929-1940
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Volume16
Issue number4
DOIs
StatePublished - 01 08 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

Keywords

  • Geometric information
  • histogram of oriented gradient (HOG)
  • near infrared (NIR)
  • nighttime
  • part based
  • pedestrian detection
  • spatial relationship

Fingerprint

Dive into the research topics of 'Near-Infrared-Based Nighttime Pedestrian Detection Using Grouped Part Models'. Together they form a unique fingerprint.

Cite this