Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier

This paper presents a reliable and real-time method to detect pedestrians in image scenes that can vary greatly in appearance. To achieve greater reliability in what can be detected, a combination of visual cues is used in conjunction with edge-based features and colour information as a basis for training a random forest (RF) classifier to detect the local contour cues for pedestrian images. To achieve a real-time detection rate, the contour cues, edge-based features and colour information are incorporated and then trained using a cascade RF classifier with a census transform histogram visual descriptor that implicitly captures the global contours of the pedestrians. The contour detector favourably exceeded previous leading contour detectors and achieved a 95% detection rate. The reliability and specificity of the pedestrian detector are demonstrated on more than 5000 positive images containing street furniture, lamp posts and trees, structures that are frequently confused with persons by computer vision systems. Evaluation with over 220 video sequences with 640 x 480 pixel resolution presented a true positive rate of 96%. The proposed pedestrian detector outperforms previous competitive pedestrian detectors on many varied person data sets. The speed of execution in a robot is about 62 ms per frame for images of 640 x 480 pixels on an Intel Core i3-2310M (TM) processor running at 2.10 GHz with a RAM of 4 GB.