Adaptive Contour Features in oriented granular space for human detection and segmentation

In this paper, a novel feature named adaptive contour feature (ACF) is proposed for human detection and segmentation. This feature consists of a chain of a number of granules in oriented granular space (OGS) that is learnt via the AdaBoost algorithm. Three operations are defined on the OGS to mine o...

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Bibliographic Details
Published in2009 IEEE Conference on Computer Vision and Pattern Recognition pp. 1786 - 1793
Main Authors Wei Gao, Haizhou Ai, Shihong Lao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2009
Subjects
Boosting
Detectors
Face detection
Heuristic algorithms
Humans
Image edge detection
Object detection
Robustness
Shape
Space technology
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Summary:In this paper, a novel feature named adaptive contour feature (ACF) is proposed for human detection and segmentation. This feature consists of a chain of a number of granules in oriented granular space (OGS) that is learnt via the AdaBoost algorithm. Three operations are defined on the OGS to mine object contour feature and feature co-occurrences automatically. A heuristic learning algorithm is proposed to generate an ACF that at the same time define a weak classifier for human detection or segmentation. Experiments on two open datasets show that the ACF outperform several well-known existing features due to its stronger discriminative power rooted in the nature of its flexibility and adaptability to describe an object contour element.
ISBN:1424439922
9781424439928
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2009.5206762