# Camera Calibration by Global Constraints on the Motion of Silhouettes

## TL;DR

This paper presents a novel silhouette-based method for camera calibration that significantly improves accuracy by reducing outliers through constrained flow optimization, validated on multiple datasets.

## Contribution

Introduces a linear integer programming approach for silhouette motion analysis that enhances camera calibration accuracy by effectively reducing outliers.

## Key findings

- Achieved two orders of magnitude improvement over previous methods.
- Validated on four standard datasets with diverse viewpoints.
- Provided accurate calibration results across different scenarios.

## Abstract

We address the problem of epipolar geometry using the motion of silhouettes. Such methods match epipolar lines or frontier points across views, which are then used as the set of putative correspondences. We introduce an approach that improves by two orders of magnitude the performance over state-of-the-art methods, by significantly reducing the number of outliers in the putative matching. We model the frontier points' correspondence problem as constrained flow optimization, requiring small differences between their coordinates over consecutive frames. Our approach is formulated as a Linear Integer Program and we show that due to the nature of our problem, it can be solved efficiently in an iterative manner. Our method was validated on four standard datasets providing accurate calibrations across very different viewpoints.