Human Detection Using Wavelet-Based CS-LBP and a Cascade of Random Forests

In this paper, we propose a novel human detection approach combining wavelet-based center symmetric LBP (WCS-LBP) with a cascade of random forests. To detect human regions, we first extract three types of WCS-LBP features from a scanning window of wavelet transformed sub-images to reduce the feature dimension. Then, the extracted WCS-LBP descriptors are applied to a cascade of random forests, which are ensembles of random decision trees. Using a cascade of random forests with WCS-LBP, human detection is performed in near real-time, and the detection accuracy is also increased, as compared to combinations of other features and classifiers. The proposed algorithm is successfully applied to various human and non-human images from the INRIA dataset, and it performs better than other related algorithms.