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