Stereo-based Pedestrian Detection using Multiple Patterns

Detecting pedestrians from a moving vehicle is a challenging problem since the essence of the task is to search non-rigid moving objects with various appearances in a dynamic and outdoor environment. In order to alleviate these difficulties, we propose a new human detection framework which makes the most use of stereo vision. While the conventional stereo-based detection methods initially generate regions of interest or ROIs on one of stereo images, the proposed one defines the ROIs on both left and right images. This paper presents two different ways for utilizing the stereo ROIs. The first one is to classify the stereo ROIs individually and integrate the classification scores to obtain the final decision. The second one is to extend gradient-based local descriptors [1, 14] to multiple views and present new feature descriptors which we call Stereo HOG and Stereo CoHOG. Through experiments we show that both methods significantly reduce the false alarm rate while keeping the detection rate comparing with monocular-based methods.