A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking

TL;DR

This paper introduces a real-time monocular 6DOF object pose tracking algorithm using a Gauss-Newton optimization scheme on region-based color histograms, outperforming existing methods especially in cluttered scenes.

Contribution

It presents a novel Gauss-Newton optimization approach for region-based pose tracking and introduces a new dataset for monocular object tracking in dynamic, cluttered environments.

Findings

Gauss-Newton optimization yields faster convergence and higher accuracy.

The method outperforms existing approaches in cluttered and occluded scenes.

A new dataset for monocular pose tracking is introduced.

Abstract

We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects using a monocular RGB camera. The key idea is to derive a region-based cost function using temporally consistent local color histograms. While such region-based cost functions are commonly optimized using first-order gradient descent techniques, we systematically derive a Gauss-Newton optimization scheme which gives rise to drastically faster convergence and highly accurate and robust tracking performance. We furthermore propose a novel complex dataset dedicated for the task of monocular object pose tracking and make it publicly available to the community. To our knowledge, it is the first to address the common and important scenario in which both the camera as well as the objects are moving simultaneously in cluttered scenes. In numerous experiments - including our own proposed dataset - we demonstrate that the proposed Gauss-Newton approach outperforms existing approaches, in particular in the presence of cluttered backgrounds, heterogeneous objects and partial occlusions.