Nonlinear L1-norm minimization learning for human detection

View, appearance and pose variations make it difficult to detect human objects only by using linear classification methods. Inspired by the successful applications of L1-norm minimization learning (LML) for human detection, we propose a new nonlinear L1-norm minimization learning method (NL-LML). It integrates a nonlinear transformation with an LML optimization model for human detection. The NL-LML method first maps the samples into a space based on the kernel function, and then combines the reformulated samples in the transformed space with the LML model to learn a classifier. Histograms of orientated gradient (HOG) features are used as the feature descriptors, and the sliding window scheme is adopted to detect humans in images. Experiments on two human datasets validate the efficiency and effectiveness of the proposed method.