Localization of pedestrian area from hybrid camera system

Accurate pedestrian path prediction and motion estimation are important tasks in the intelligent vehicle domain. The model incorporates the pedestrian pose recognition and lateral speed, motion direction and spatial layout of the environment. Pedestrian poses are recognized according to the spatial body language ratio. The region of interest of detected human is used. Then the centroid of the body relative to its width and height is used to define the pedestrian pose. Motion trajectory is obtained by using point tracking on that centroid, and then motion direction and estimated velocity are determined. Spatial layout is determined by the location of pedestrian with respect to road boundary. Input vector pedestrian pose, motion direction and speed, and the distance from the road lane are used by linear dynamic system. Instead of combining a number of subjects in a single model that will have to deal with the stylistic variations, all the subjects are separately trained in individual models. These models will be then hierarchically separated according to their action. Then, its classification will constrain the models to use for the prediction. Hybrid camera system is performed. A perspective camera is used for pedestrian localization with respect to car speed. And then an omnidirectional camera is used to enlarge field of view of monocular camera. Pedestrians are tracked in 360 degree around the vehicle. Experimental results show that the system has path predictions accuracy with the largest mean errors, 25 cm, for bending trajectories.