Three-level cascade of random forests for rapid human detection

Abstract

We propose a novel human detection approach that combines three types of center symmetric local binary patterns (CS-LBP) descriptors with a cascade of random forests (RFs). To detect human regions in a low-dimensional feature space, we first extract three types of CS-LBP features from the scanning window of a downsampled saliency texture map and two wavelet-transformed subimages. The extracted CS-LBP descriptors are applied to a three-level cascade of RFs, which combines a series of RF classifiers as a filter chain. The three-level cascade of RFs with CS-LBPs delivers rapid human detection with higher detection accuracy, as compared with combinations of other features and classifiers. The proposed algorithm is successfully applied to various human and nonhuman images from the INRIA dataset, and it performs better than other related algorithms.