Human detection in images via L1-norm Minimization Learning

In recent years, sparse representation originating from signal compressed sensing theory has attracted increasing interest in computer vision research community. However, to our best knowledge, no previous work utilizes L1-norm minimization for human detection. In this paper we develop a novel human detection system based on L1-norm Minimization Learning (LML) method. The method is on the observation that a human object can be represented by a few features from a large feature set (sparse representation). And the sparse representation can be learned from the training samples by exploiting the L1-norm Minimization principle, which can also be called feature selection procedure. This procedure enables the feature representation more concise and more adaptive to object occlusion and deformation. After that a classifier is constructed by linearly weighting features and comparing the result with a calculated threshold. Experiments on two datasets validate the effectiveness and efficiency of the proposed method.