Abstract: As the biological vision features shows superior performance on object classification, a multiclass and multiview object detection approach based on sparse biological vision features was adopted. Firstly, the standard model of biological vision was improved with the technique of sparse features, which improved the separability of object effectively. Then, the object detector based on sparse biological vision features was designed with the technique of sliding window, and the detection task was completed via local neighborhood suppression algorithm. At last, the multiclass and multiview object detection task was accomplished through building object dictionary and designing several object detectors in the scene. The experimental results show that the proposed approach exhibits a robust performance.