Spectrum analysis of motion parallax in a 3D cluttered scene and application to egomotion.
Previous methods for estimating observer motion in a rigid 3D scene assume that image velocities can be measured at isolated points. When the observer is moving through a cluttered 3D scene such as a forest, however, pointwise measurements of image velocity are more challenging to obtain because multiple depths, and hence multiple velocities, are present in most local image regions. We introduce a method for estimating egomotion that avoids pointwise image velocity estimation as a first step. In its place, the direction of motion parallax in local image regions is estimated, using a spectrum-based method, and these directions are then combined to directly estimate 3D observer motion. There are two advantages to this approach. First, the method can be applied to a wide range of 3D cluttered scenes, including those for which pointwise image velocities cannot be measured because only normal velocity information is available. Second, the egomotion estimates can be used as a posterior constraint on estimating pointwise image velocities, since known egomotion parameters constrain the candidate image velocities at each point to a one-dimensional rather than a two-dimensional space.
Mann
Richard
R
School of Computer Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada. mannr@uwaterloo.ca
Langer
Michael S
MS
D000465:Algorithms; D016000:Cluster Analysis; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008124:Locomotion; D009039:Motion Perception; D009949:Orientation; D010363:Pattern Recognition, Automated / Q000379:methods*; D012371:Robotics / Q000379:methods; D013057:Spectrum Analysis / Q000379:methods