Pedestrian detection from non-smooth motion

Pedestrian detection has been intensively studied based on appearances for driving safety. Only a few works have explored between-frame optical flow as one of features for human classification. In this paper, however, a new point of view is taken to watch a longer period for non-smooth movement. We explore the pedestrian detection purely based on motion, which is common and intrinsic for all pedestrians regardless of their shape, color, background, etc. We found unique motion characteristics of humans different from rigid objects in motion profiles. Based on the explicit analysis of spatial-temporal behaviors of pedestrians, non-smooth motion points are detected at the motion trajectories of limbs and body. This method works for driving video where both pedestrians and background are moving, and it yields good results as it is less influenced from pedestrian variations in shape and environment. The method also has low computational cost and it can be combined with a shape-based method as pre-screening tool for accuracy and speed.