Today I read a paper titled “Automatic creation of urban velocity fields from aerial video”
The abstract is:
In this paper, we present a system for modelling vehicle motion in an urban scene from low frame-rate aerial video.
In particular, the scene is modelled as a probability distribution over velocities at every pixel in the image.
We describe the complete system for acquiring this model.
The video is captured from a helicopter and stabilized by warping the images to match an orthorectified image of the area.
A pixel classifier is applied to the stabilized images, and the response is segmented to determine car locations and orientations.
The results are fed in to a tracking scheme which tracks cars for three frames, creating tracklets.
This allows the tracker to use a combination of velocity, direction, appearance, and acceleration cues to keep only tracks likely to be correct.
Each tracklet provides a measurement of the car velocity at every point along the tracklet’s length, and these are then aggregated to create a histogram of vehicle velocities at every pixel in the image.
The results demonstrate that the velocity probability distribution prior can be used to infer a variety of information about road lane directions, speed limits, vehicle speeds and common trajectories, and traffic bottlenecks, as well as providing a means of describing environmental knowledge about traffic rules that can be used in tracking.