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 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.