  We developed a machine vision system to automatically capture the dynamics of
pedestrians under four different traffic scenarios. By considering the overhead
view of each pedestrian as a digital object, the system processes the image
sequences to track the pedestrians. Considering the perspective effect of the
camera lens and the projected area of the hallway at the top-view scene, the
distance of each tracked object from its original position to its current
position is approximated every video frame. Using the approximated distance and
the video frame rate (30 frames per second), the respective velocity and
acceleration of each tracked object are later derived. The quantified motion
characteristics of the pedestrians are displayed by the system through
2-dimensional graphs of the kinematics of motion. The system also outputs video
images of the pedestrians with superimposed markers for tracking. These visual
markers were used to visually describe and quantify the behavior of the
pedestrians under different traffic scenarios.
