Real-time
Optical Flow Applications
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Accurate
optical flow estimation is a crucial task for
many computer vision applications. However,
because of its computational power and
processing speed requirements, it is rarely used
for real-time obstacle detection, especially for
small unmanned vehicle and embedded
applications. Two hardware-friendly vision
algorithms are proposed in this paper to address
this challenge. A ridge regression-based optical
flow algorithm is developed to cope with the
existing collinear problem in traditional least
squares approaches for calculating optical flow.
Additionally, taking advantage of hardware
parallelism, spatial and temporal smoothing
operations are applied to image sequence
derivatives to improve accuracy. An efficient
motion field analysis algorithm using the
optical flow values and based on a simplified
motion model is also developed and implemented
in hardware. The resulting obstacle detection
algorithm is specifically designed for ground
vehicles moving on planar surfaces. Results from
the software simulations and hardware execution
of the two proposed algorithms prove that with
adequate hardware, a low power, compact obstacle
detection sensor can be realized for small
unmanned vehicles and embedded applications.
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Project Sponsors:
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David and Deborah Huber Scholarship
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Collaborators:
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Graduate Students:
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Zhaoyi Wei
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Publications:
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Z.Y.
Wei, D.J. Lee, B.E. Nelson, and J.K Archibald,
“Hardware-Friendly Vision Algorithms for
Embedded Obstacle Detection Applications,”
IEEE Transactions on Circuits and Systems for
Video Technology, vol. 20/11, p. 1577-1589,
November 2010.
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Z.Y.
Wei, D.J. Lee, and B.E. Nelson,
“Accurate Optical Flow Sensor for Obstacle
Avoidance,“ Lecture Notes in Computer Science
(LNCS), Part I, LNCS 5358, p. 240-247,
International Symposium on Visual Computing
(ISVC), Las Vegas, NV, U.S.A., December 1-3,
2008.
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Z.Y.
Wei, D.J. Lee, D.J. Jilk, and R.B.
Schoenberger, “Motion Projection for
Floating Object Detection,” Lecture
Notesin Computer Science (LNCS), Part II, LNCS
4842, p. 152-161, International Symposium on
Visual Computing (ISVC), Lake Tahoe, CA,
U.S.A., November 26-28, 2007.
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