Implicit color segmentation features for pedestrian and object detection
Patrick Ott and Mark Everingham
In: ICCV 2009, 29 Sep - 02 Oct 2009, Kyoto, Japan.
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 detection on the PASCAL VOC2006 dataset also show substantial improvements in accuracy.
|EPrint Type:||Conference or Workshop Item (Poster)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Mark Everingham|
|Deposited On:||08 March 2010|