Novel outline features for pedestrian detection system with thermal images

Chun Fu Lin*, Chin Sheng Chen, Wen Jyi Hwang, Chih Yen Chen, Chi Hung Hwang, Chun Li Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Recently, the need of pedestrian detection at night has gained more and more interest. However, the performance of traditional nighttime pedestrian detection systems remains poor because region of interest (ROI) generation and feature extraction are designed separately. Thus, this paper presents novel thermal imagery algorithms to enhance the performance of nighttime pedestrian detection systems. The proposed thermal image pedestrian detection system involves novel outline features, developed from the ROI generation method of pedestrians that are different from traditional features. A three-layer back-propagation feed-forward neural network is used as the classifier. Two databases, the OTCBVS database and our own are used to evaluate the performance of the proposed thermal image pedestrian detection algorithm. Experimental results show that the proposed outline features are effective, and the detection performance of a traditional pedestrian detection system at night is improved.

Original languageEnglish
Pages (from-to)3440-3450
Number of pages11
JournalPattern Recognition
Issue number11
Publication statusPublished - 2015 Nov 1


  • Outline features
  • Pedestrian detection at night
  • Thermal images

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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