Human detection based on pyramidal statistics of oriented filtering and online learned scene geometrical model

We study the problem of robust human detection. In this paper, a new descriptor, Pyramidal Statistics of Oriented Filtering (PSOF), is proposed for human shape representation. Unlike traditional one-scale gradient-based methods, the PSOF descriptor utilizes a Gabor filter bank to obtain multi-scale pixel-level orientation information and makes use of locally normalized pyramidal statistics of these Gabor responses to represent object shape, which shows great robustness to image noise and blur. Besides, to exclude detection outliers that violate perspective projection in image sequence, a geometrical model is learned online to describe the relationship between object’s average height and the foot-point coordinate. Experimental results on both static images and video sequences show that PSOF detector performs much better than one of the state-of-the-art detectors.