A novel set of pixel difference-based features for pedestrian detection

Xing Liu, Kar Ann Toh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We propose a feature extraction pipeline namely Difference Matrix Projection (DMP)for pedestrian detection. The goal is to design an effective feature set that can be efficiently computed. Our feature consists of pixel differences at different scales and orientations coupled with average pooling and block normalization. We develop a formulation that computes the difference maps and local average using global image projection instead of an iterative filtering. As a result, the computations can be expressed by analytic equations in terms of the input image. The projection matrices are pre-constructed, so they are readily applied to the image for feature extraction. Experiment results on the Daimler Chrysler (Daimler-CB) and NICTA pedestrian datasets show encouraging results, especially for low-resolution samples.

Original languageEnglish
Title of host publication2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538622483
DOIs
Publication statusPublished - 2018 Mar 9
Event4th IEEE International Conference on Identity, Security, and Behavior Analysis, ISBA 2018 - Singapore, Singapore
Duration: 2018 Jan 112018 Jan 12

Publication series

Name2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis, ISBA 2018
Volume2018-January

Other

Other4th IEEE International Conference on Identity, Security, and Behavior Analysis, ISBA 2018
CountrySingapore
CitySingapore
Period18/1/1118/1/12

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Founda- tion of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant number: NRF-2015R1D1A1A09061316).

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Safety, Risk, Reliability and Quality
  • Behavioral Neuroscience
  • Social Sciences (miscellaneous)

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