Stereo-Vision-Based Pedestrian's Intention Detection in a Moving Vehicle

We present a method to detect starting, stopping and bending in intentions of pedestrians from a moving vehicle based on stereo-vision. The method focuses on urban scenarios where these pedestrian movements are common and may result in critical situations. Pedestrian intentions are determined by means of an image-based motion contour histogram of oriented gradient descriptor. It is based on silhouettes gathered from stereo data and does not require any compensation of appearance changes resulting from the ego-motion of a vehicle. Nevertheless, it covers small movements indicating a pedestrian's intention. A linear support vector machine with probabilistic estimates is used for classification. We evaluated our method on the publicly available Daimler Pedestrian Path Prediction Benchmark Dataset containing detections of a stateof-the-art pedestrian detector. We detect a pedestrian's stopping intention from 125 ms to 500 ms before standing still within an accuracy range of 80% to 100%. Bending in is detected from 320 ms to 570 ms after a first visible lateral body movement in the same accuracy range. The intention to cross the road from standing still (starting) is detected 250 ms after the first visible motion and, therefore, within the first step with an accuracy of 100%.