The invention discloses a pedestrian detection method based on layered kernel sparse representation. The method is characterized by comprising steps as follows: preprocessing acquired traffic videos to obtain required positive and negative samples, acquiring multi-scale eigenvectors through layered sub-block division, and constructing two kinds of dictionary matrixes; preprocessing to-be-detected pedestrian images to obtain test samples, and performing pedestrian feature extraction with a method adopted in the dictionary construction process to form eigenvectors of the test samples; performing kernel sparse decomposition on the eigenvectors of the test samples with histogram cross kernel functions, adopting Gaussian functions for weighting in an iteration solution process, and reconstructing errors to realize pedestrian detection. With the adoption of the method, the higher detection performance can be realized, the pedestrian detection accuracy under the condition of partial shielding is effectively improved, and the robustness of a pedestrian detection system for appearance changes is enhanced.