In this paper, the researchers brought out a novel pedestrian detection framework for the advanced driver assistance system of mobile platform under the normal urban street environment. Different from the conventional systems that focus on the pedestrian detection at near distance by interfusing multiple sensors (such as radar, laser and infrared camera), our system has achieved the pedestrian detection at all (near, middle and long) distance on a normally driven vehicle (1–40 km/h) with monocular camera under the street scenes. Since pedestrians typically exhibit not only their human-like shape but also the unique human movements generated by their legs and arms, we use the spatio-temporal histogram of oriented gradient (STHOG) to describe the pedestrian appearance and motion features. The shape and movement of a pedestrian will be described by a unique feature produced by concatenating the spatial and temporal histograms. A STHOG detector trained by the AdaBoost algorithm will be applied to the images stabilized by the structure from motion (SfM) algorithm with geometric ground constraint. The main contributions of this work include: (1) ground constraint with monocular camera to reduce the computational cost and false alarms; (2) preprocessing by stabilizing the successive images captured from mobile camera with the SfM algorithm; (3) long-distance (maximum 100 m) pedestrian detection at various velocities (1–40 km/h). Through the extensive experiments under different city scenes, the effectiveness of our algorithm has been proved.

This research was published on Machine Vision and Application  (2015) 26:161–183 titled Onboard monocular pedestrian detection by combining spatio-temporal hog with structure from motion algorithm