follows video action
Technology Research News
Teaching a computer to watch video is a
lot harder than it might seem. What is obviously a face or a car or a
sunset to us is often a confusing blur of shadows, colors and shapes to
Although computers are good at recognizing light, dark and color, and
they do a reasonable job of picking out objects in still images, they
get lost when objects move around. This is because the appearance of a
moving object is constantly changing.
The key to giving computers a decent shot at following the action in a
video is getting them to track the objects.
A team of researchers at the University of Amsterdam in the Netherlands
has come up with an object-tracking technique that works by capturing
the essence of a video object rather than just relying on its shape.
The underlying assumption of most video object tracking research, which
focuses on the position and pose of an object, is that the object's appearance
does not change much, said Marcel Worring, an assistant professor of computer
science that the University of Amsterdam.
In contrast, the researchers' approach makes a full assessment of an object
by monitoring every point, or pixel, associated with it. This makes it
easier for the computer to recognize that a face turned upward in the
sun is the same a moment later when it is angled down and in the shade.
"Our contribution is to... track the changes in appearance of every part
of the object," said Worring. "In practice this means that we track changes
in pixel values that belong to the object." Those pixel changes can be
due to changes in lighting and the orientation of the object, and whether
the object appears to have moved closer or farther away, he said.
By keeping track of the state of each pixel that makes up an object, the
system is also better able to keep tabs on the object when it momentarily
disappears, said Worring.
Like other object-tracking techniques, the researchers' system uses a
cycle of measuring an object, predicting where the object will be next,
using the prediction to narrow the area to be measured, and using the
measurements to improve the prediction. When the measurements are completely
off from the prediction, it usually means the object has disappeared from
"The system detects that the object has been lost by observing that the
prediction becomes totally useless," said Worring. "It then... keeps doing
a search in the neighborhood of the old position till the object reappears.
At that point the tracking continues." Tracking the whole of an object
rather than just its shape makes the researchers' system more efficient
at recognizing when an object has disappeared, which gives it a better
chance of picking up the object when it reappears, he said.
The system could be used for creating video hyperlinks, tracking faces
to select the best view for face recognition, and tracking cars, said
Worring. "The current system has to be tested thoroughly, but we expect
it to be ready for practical use within a year," he said.
The algorithm is useful, said Aggelos K. Katsaggelos a professor of information
technology at Northwestern University. "It's a nice combination of template
matching and temporal prediction."
Worring's research colleagues were Hieu T. Nguyen and Rein van den Boomgaard
of the University of Amsterdam. They presented the research at the International
Conference on Computer Vision in Vancouver, July 9 to 12, 2001. The research
was funded by the Dutch Organization for Scientific Research.
Timeline: <1 year
TRN Categories: Computer Vision and Image Processing
Story Type: News
Related Elements: Technical paper, "Occlusion Robust Adaptive
Template Tracking," International Conference on Computer Vision, Vancouver,
July 9 to 12, 2001
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