A monocular vision based pedestrian detection system for intelligent vehicles

Detecting pedestrians in images is a challenging task, especially for the intelligent vehicle environment where there is a moving camera. In this paper, we develop a monocular vision based pedestrian detection system for intelligent vehicles. We propose a two-stage pedestrian detection approach. A full-body pedestrian detector with Haar-like wavelet features and cascade Adaboost classifier (P. Viola and M. Jones, 2001) is trained to generate some pedestrian candidates on the image. We regard pedestrian as assembly of some parts of the body, and train five part detectors with shapelet features (P. Sabzmeydani and G. Mori, 2007) and Adaboost classifier. Each candidate is detected with these part detectors and is verified using detector ensemble (Shengyang Dai et al., 2007). Finally, after the verification, multiple detections are fused with the mean shift method. Experiments show that our system has high performance in detecting pedestrians in different poses, clothing, illumination, occlusion and background.