A RELIABLE FUSION METHOD USING BACKGROUND SUBTRACTION TECHNIQUE AND CASCADE-ADABOOST CLASSIFIER FOR REAL-TIME PEDESTRIAN DETECTION
Moving objects recognition plays an important role in camera-only active safety systems and intelligent autonomous vehicles. For these applications, reliable detection performance is required; however, pedestrian detection is challenging due to their divergent dressing and action variety. Besides, real-time detection and recognition performance is also critical. 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 features are used to classify the detected moving objects to the categories they belong to. The proposed fusion method achieves a speedup of 14x compared to conventional approaches based on Haar-Like descriptor only, and can speed up at least 2x faster computing speed as compared to previous works 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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