A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking (1807.02087v2)

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.

• The paper introduces a Gauss-Newton optimization framework that significantly improves convergence speed and tracking robustness compared to traditional gradient descent methods.
• It leverages region-based cost functions with temporally consistent local color histograms to enable precise real-time 6DOF pose tracking of rigid 3D objects.
• The introduction of the RBOT dataset with dynamic, cluttered scenarios highlights the method's practical applications in mixed reality, robotics, and human-computer interaction.

Real-Time Monocular Multiple Object Tracking via a Region-based Gauss-Newton Approach

The paper introduces an algorithm for the task of real-time 6DOF (degrees of freedom) pose tracking for rigid 3D objects utilizing a monocular RGB camera. This is achieved through the innovative application of a region-based cost function centered on temporally consistent local color histograms (tclc-histograms), diverging from traditional first-order optimization techniques by employing a Gauss-Newton optimization framework. By leveraging Gauss-Newton optimization, the authors report substantial improvements in convergence speed and tracking robustness, particularly when confronted with the complexities of cluttered environments.

A significant contribution of this work lies in the systematic derivation of the Gauss-Newton optimization as a re-weighted nonlinear least-squares problem. Traditionally, similar region-based cost functions deal with optimization through simple gradient descent, posing limitations due to manual tuning requirements for step sizes and inherent instabilities. The suggested optimization strategy convincingly enhances both the stability and efficiency of the tracking process, providing a performance edge over existing methods.

This research further introduces the RBOT dataset, which is noted for incorporating dynamic scenarios simulating simultaneous motion of both camera and objects with varying background complexities, noise levels, and mutual occlusions. Such scenarios are critical in evaluating and advancing the robustness and applicability of monocular pose tracking systems. The dataset includes synthetic sequences tailored to bridge the gap between academic research and real-world applications, providing a helpful tool for further developments in 6DOF object tracking research.

Experimental results reflect superior performance of the proposed approach over existing methodologies, as demonstrated in extensive evaluations on both the OPT dataset and the newly introduced RBOT dataset. The authors report marked improvements in tracking success rates, particularly under dynamic lighting conditions and occlusions, underscoring the effectiveness of the proposed Gauss-Newton approach. The results exhibit robustness across various objects, including those with ambiguous silhouettes and under scenarios where both real and simulated background complexities are present.

The implications of such advancement in object pose tracking are wide-reaching. Beyond the theoretical contributions, there are valuable practical applications in mixed reality, robotics, and human-computer interaction. The ability to accurately and efficiently track objects using minimal hardware expands the potential uses of mobile and lightweight systems significantly. Speculating on future developments, such methodologies could integrate with deep learning elements for further adaptation and agility in unknown environments or be expanded to work collaboratively with depth sensors for even higher accuracy and applicability in complex scenes.

The research discussed establishes a foundational advancement in monocular object tracking, presenting both a robust algorithm and supportive dataset conducive for continued exploration and application. It stands as a substantive contribution to the field, inviting further utilization and iteration by researchers seeking efficient solutions for real-time and resource-constrained environments.