A FLEXIBLE APPROACH FOR REAL-TIME PEDESTRIAN DETECTION WITH FOREGROUND-BASED CASCADE CLASSIFIER

  • Vu Hong Son Hung Yen University of Technology and Education
  • Kuan-Hung Chen Feng Chia University, Taichung 40724, Taiwan, R.O.C
Keywords: moving object detection, pedestrian detection, fusion method.

Abstract

Almost all existing state-of-the-art pedestrian detection methods require heavy computing cost from their feature descriptors, which cannot detect pedestrians reliably in real-time. In this paper, we take advantage of Background Subtraction (BS) technique to extract moving objects region on whole natural scene images in complicated environments. Then, Haar-like or Histograms of Oriented Gradients (HOG) features are used to classify the detected moving objects to the categories they belong to. The proposed fusion method achieves a speedup of at least 4.5x compared to conventional approaches based on Haar-Like and HOG descriptors only for high resolution images (768 x 576), with detection rate of 97.76% and a minor false detection rate of 2.66%.

References

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Published
2017-01-12
How to Cite
Vu Hong Son, & Kuan-Hung Chen. (2017). A FLEXIBLE APPROACH FOR REAL-TIME PEDESTRIAN DETECTION WITH FOREGROUND-BASED CASCADE CLASSIFIER. UTEHY Journal of Science and Technology, 12, 62-69. Retrieved from http://tapchi.utehy.edu.vn/index.php/jst/article/view/236