Pedestrian Detection Using Pixel Difference Matrix Projection

Xing Liu, Kar Ann Toh, Jan P. Allebach

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Pedestrian detection in the embedded system, such as video surveillance equipment, usually involves low-resolution pedestrian samples and requires a low computational cost. Many pedestrian detectors rely on a large feature pool and suffer in their efficiency and performance for real-time monitoring. In this paper, a set of light-weight features is proposed to enhance the pedestrian detection performance when a small-medium scale of training data with low-resolution images is available. To address this issue, a difference matrix projection (DMP) is developed to compute aggregated multi-oriented pixel differences using global matrix operations. Both the pixel differences and aggregation are computed using global matrix projection to avoid the laborious iterative operations. We tested our method on the INRIA, Daimler Chrysler classification (Daimler-CB), NICTA, and Caltech Pedestrian datasets. The experiments on these benchmark data sets show encouraging results in terms of detection performance, particularly for image datasets with low-resolution pedestrians.

Original languageEnglish
Article number8703888
Pages (from-to)1441-1454
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number4
Publication statusPublished - 2020 Apr

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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