Camera Calibration from Dynamic Silhouettes Using Motion Barcodes

TL;DR

This paper introduces a novel method using motion barcodes for lines to efficiently and accurately compute epipolar geometry from dynamic silhouettes, especially when traditional point matching is difficult.

Contribution

It proposes a new temporal signature, motion barcode for lines, that significantly speeds up and improves robustness in epipolar geometry estimation from dynamic silhouettes.

Findings

Speed increased by about two orders of magnitude

Enhanced robustness and accuracy in epipolar geometry computation

Effective matching of epipolar lines using motion barcodes

Abstract

Computing the epipolar geometry between cameras with very different viewpoints is often problematic as matching points are hard to find. In these cases, it has been proposed to use information from dynamic objects in the scene for suggesting point and line correspondences. We propose a speed up of about two orders of magnitude, as well as an increase in robustness and accuracy, to methods computing epipolar geometry from dynamic silhouettes. This improvement is based on a new temporal signature: motion barcode for lines. Motion barcode is a binary temporal sequence for lines, indicating for each frame the existence of at least one foreground pixel on that line. The motion barcodes of two corresponding epipolar lines are very similar, so the search for corresponding epipolar lines can be limited only to lines having similar barcodes. The use of motion barcodes leads to increased speed, accuracy, and robustness in computing the epipolar geometry.