Superpixels in Pedestrian Detection from Stereo Images in Urban Traffic Scenarios

Pedestrian detection is a common task in every driving assistance system. The main goal resides in obtaining a high accuracy detection in a reasonable amount of processing time. This paper proposes a novel method for superpixel-based pedestrian hypotheses generation and their validation through feature classification. We analyze the possibility of using superpixels in pedestrian detection by investigating both the execution time and the accuracy of the results. Urban traffic images are acquired by a stereo-cameras system. A multi-feature superpixels-based method is used for obstacles segmentation and pedestrian hypotheses selection. Histogram of Oriented Gradients features are extracted both on the raw 2D intensity image and also on the superpixels mean intensity image for each hypothesis. Principal Component Analysis is also employed for selecting the relevant features. Support Vector Machine and AdaBoost classifiers are trained on: initial features and selected features extracted from both raw 2D intensity image and mean superpixels intensity image. The comparative results show that superpixelsbased pedestrian detection clearly reduce the execution time while the quality of the results is just slightly decreased.