Abstract | ||
---|---|---|
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. |
Year | Venue | Keywords |
---|---|---|
2009 | Clinical Orthopaedics and Related Research | pattern recognition,velocity field,probability distribution |
Field | DocType | Volume |
Histogram,Computer vision,Aerial video,Image warping,Pattern recognition,Computer science,Probability distribution,Pixel,Artificial intelligence,Acceleration,Classifier (linguistics),Orthophoto | Journal | abs/0912.1 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Edward Rosten | 1 | 1599 | 76.62 |
Rohan Loveland | 2 | 15 | 1.10 |
Mark D. Hickman | 3 | 31 | 4.81 |