Discriminative Weighted Sparse Partial Least Squares for Human Detection

Channel feature detectors have shown great advantages in human detection. However, a large pool of channel features extracted for human detection usually contains many redundant and irrelevant features. To address this issue, we propose a robust discriminative weighted sparse partial least square approach for feature selection and apply it to human detection. Unlike partial least squares (PLS), which is a straightforward dimensionality reduction technique, we propose using sparse PLS to achieve feature selection. Furthermore, in order to obtain a robust latent matrix, we formulate a discriminative regularized weighted least square problem, where a discriminative term is incorporated to effectively distinguish positive samples from negative samples. A robust sparse weight matrix is trained based on the latent matrix and used for feature selection. Finally, we use the selected channel features to train the boosted decision trees and incorporate the weights of selected features with each tree. The human detector trained by the selected features can preserve high robustness and discriminativeness. Experimental results on some challenging human data sets demonstrate that the proposed approach is effective and achieves state-of-the-art performance.