Robust Vehicle Tracking in Video Images Being Taken from a Helicopter

Measuring positions, velocities and accelerations/decelerations of individual vehicles in congested traffic with standard traffic monitoring equipment, such as inductive loops, are not feasible. The behavior of drivers in the different traffic situations, as re-quired for microscopic traffic flow models, is still not sufficiently known. Remote sensing and computer vision technology are recently being used to solve this problem. In this study we use video images taken from a helicopter above a fixed point of the highway. We address the problem of tracking the movement of previously detected vehicles through a stabilized video sequence. We combine two approaches, optical flow and matching based tracking, improve them by adding constraints and using scale space. Feature elements, i.e. the corners, lines, regions and outlines of each car, are extracted first. Then, optical-flow is used to find for each pixel in the interior of a car the corresponding pixel in the next image, by inserting the brightness model. Normalized cross correlation matching is used at the corners of the car. Different pixels are used for solving the aperture problem of optical flow and for the template matching area: neighboring pixel and feature pixels. The image boundary, road line boundaries, maximum speed of the car, and positions of surrounding cars are used as constraints. Ideally, the result of each pixel of a car should give the same displacement because cars are rigid objects.