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GTC ON-DEMAND

Computer Vision
Presentation
Media
Dense Point Trajectories by GPU-Accelerated Large Displacement Optical Flow
Speakers:
Narayanan Sundaram
- University of California, Berkeley
Abstract:
In this poster we discuss a method for computing point trajectories based on a fast parallel implementation of a recent optical flow algorithm that tolerates fast motion. The parallel implementation of large displacement optical flow runs about 78x faster than the serial C++ version. We use this implementation is a point tracking application. Our resulting technique tracks up to three orders of magnitude more points and is 46% more accurate than the Kanade-Lucas-Tomasi tracker. Compared to the Particle Video tracker, we achieve 66% better accuracy while retaining the ability to handle large displacements while running an order of magnitude faster.
 
Topics:
Computer Vision
Type:
Poster
Event:
GTC Silicon Valley
Year:
2010
Session ID:
P10F02
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