Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax
Abstract
We present a novel approach to detect and track independently
moving regions in a 3D scene observed by a moving
camera in the presence of strong parallax. Detected
moving pixels are classified into independently moving regions
or parallax regions by analyzing two geometric constraints:
the commonly used epipolar constraint, and the
structure consistency constraint. The second constraint is
implemented within a ˇ°Plane+Parallaxˇ± framework and
represented by a bilinear relationship which relates the image
points to their relative depths. This newly derived relationship
is related to trilinear tensor, but can be enforced
into more than three frames. It does not assume a constant
reference plane in the scene and therefore eliminates
the need for manual selection of reference plane. Then, a
robust parallax filtering scheme is proposed to accumulate
the geometric constraint errors within a sliding window and
estimate a likelihood map for pixel classification. The likelihood
map is integrated into our tracking framework based
on the spatio-temporal Joint Probability Data Association
Filter (JPDAF). This tracking approach infers the trajectory
and bounding box of the moving objects by searching
the optimal path with maximum joint probability within a
fixed size of buffer. We demonstrate the performance of the
proposed approach on real video sequences where parallax
effects are significant.