Computing the epipolar geometry between cameras with very different viewpoints is often very difficult. The appearance of objects can vary greatly, and it is difficult to find corresponding feature points. Prior methods searched for corresponding epipolar lines using points on the convex hull of the silhouette of a single moving object. These methods fail when the scene includes multiple moving objects. This paper extends previous work to scenes having multiple moving objects by using the "Motion Barcodes", a temporal signature of lines. Corresponding epipolar lines have similar motion barcodes, and candidate pairs of corresponding epipoar lines are found by the similarity of their motion barcodes. As in previous methods we assume that cameras are relatively stationary and that moving objects have already been extracted using background subtraction.
Sinha and Pollefeys @cite_9 used silhouettes to calibrate a network of cameras, assuming a single moving silhouette in a video. Each RANSAC iteration takes a different frame and samples two pairs of corresponding tangent lines to the convex hull of the silhouette @cite_5 . The intersection of each pair of lines proposes an epipole.
@cite_5 @cite_9
2063409146 2085571680
For smooth curved surfaces the dominant image feature is the apparent contour, or outline. This is the projection of the contour generator, the locus of points on the surface which separate visible and occluded parts. The contour generator is dependent of the local surface geometry and the viewpoint. Each viewpoint will generate a different contour generator. This paper addresses the problem of recovering the three–dimensional shape and motion of curves and surfaces from image sequences of apparent contours. @PARASPLIT For known viewer motion the visible surfaces can then be reconstructed by exploiting a spatio–temporal parametrization of the apparent contours and contour generators under viewer motion. A natural parametrization exploits the contour generators and the epipolar geometry between successive viewpoints. The epipolar parametrization leads to simplified expressions for the recovery of depth and surface curvatures from image velocities and accelerations and known viewer motion. @PARASPLIT The parametrization is, however, degenerate when the apparent contour is singular since the ray is tangent to the contour generator and at frontier points when the epipolar plane is a tangent plane to the surface. At these isolated points the epipolar parametrization can no longer be used to recover the local surface geometry. This paper reviews the epipolar parametrization and shows how the degenerate cases can be used to recover surface geometry and unknown viewer motion from apparent contours of curved surfaces. Practical implementations are outlined. In this paper we present an automatic method for calibrating a network of cameras that works by analyzing only the motion of silhouettes in the multiple video streams. This is particularly useful for automatic reconstruction of a dynamic event using a camera network in a situation where pre-calibration of the cameras is impractical or even impossible. The key contribution of this work is a RANSAC-based algorithm that simultaneously computes the epipolar geometry and synchronization of a pair of cameras only from the motion of silhouettes in video. Our approach involves first independently computing the fundamental matrix and synchronization for multiple pairs of cameras in the network. In the next stage the calibration and synchronization for the complete network is recovered from the pairwise information. Finally, a visual-hull algorithm is used to reconstruct the shape of the dynamic object from its silhouettes in video. For unsynchronized video streams with sub-frame temporal offsets, we interpolate silhouettes between successive frames to get more accurate visual hulls. We show the effectiveness of our method by remotely calibrating several different indoor camera networks from archived video streams.