Implicit Color Segmentation Features for Pedestrian Detection

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Implicit Color Segmentation Features for Pedestrian Detection
We investigate the problem of pedestrian detection in still images. Sliding window classifiers, notably using the Histogram-of-Gradient (HOG) features proposed by Dalal and Triggs are the state-of-the-art for this task, and we base our method on this approach. We propose a novel feature extraction scheme which computes implicit ‘soft segmentations’ of image regions into foreground/background. The method yields stronger object/background edges than grayscale gradient alone, suppresses textural and shading variations, and captures local coherence of object appearance. The main contributions of our work are: (i) incorporation of segmentation cues into object detection; (ii) integration with classifier learning cf. a post-processing filter; (iii) high computational efficiency. We report results on the INRIA person detection dataset, achieving state-of-the-art results considerably exceeding those of the original HOG detector. Preliminary results for generic object detec...
Patrick Ott and Mark Everingham
Added 13 Jul 2009
Updated 10 Jan 2010
Type Conference
Year 2009
Where ICCV
Authors Patrick Ott and Mark Everingham
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