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Towards real-time DPM object detector for driver assistance

Automatic object detection is a rapidly evolving area in surveillance and autonomous vehicles. Deformable part model (DPM) is a well-known object detector for its high precision and speed bottleneck. This paper proposes a very fast object detection pipeline based on complementary techniques to accelerate DPM. A recent fast feature pyramid technique is employed with look-up table HOG features, Fast Fourier Transform and early classification technique to speed up DPM and maintain its accuracy. We exploit SIMD optimization and multiple cores to achieve a real time detector. Our results shows that we achieve a speed-up of 50x and 6x on a single core over DPM and cascade DPM respectively. Our optimized version processes a 640×480 pixel image at 38 fps.