Simultaneous Multi-View Camera Pose Estimation and Object Tracking with Square Planar Markers

TL;DR

This paper introduces a novel method for simultaneous multi-view camera pose estimation and object tracking using square planar markers, enabling accurate, real-time tracking with low-resolution cameras in various applications.

Contribution

The method automatically estimates 3D marker configurations, camera extrinsics, and object pose from video sequences, simplifying multi-camera and multi-marker tracking setups.

Findings

High accuracy in parameter estimation with low-resolution cameras

Real-time object tracking with low computational cost

Effective in applications like augmented reality and robotics

Abstract

Object tracking is a key aspect in many applications such as augmented reality in medicine (e.g. tracking a surgical instrument) or robotics. Squared planar markers have become popular tools for tracking since their pose can be estimated from their four corners. While using a single marker and a single camera limits the working area considerably, using multiple markers attached to an object requires estimating their relative position, which is not trivial, for high accuracy tracking. Likewise, using multiple cameras requires estimating their extrinsic parameters, also a tedious process that must be repeated whenever a camera is moved. This work proposes a novel method to simultaneously solve the above-mentioned problems. From a video sequence showing a rigid set of planar markers recorded from multiple cameras, the proposed method is able to automatically obtain the three-dimensional configuration of the markers, the extrinsic parameters of the cameras, and the relative pose between the markers and the cameras at each frame. Our experiments show that our approach can obtain highly accurate results for estimating these parameters using low resolution cameras. Once the parameters are obtained, tracking of the object can be done in real time with a low computational cost. The proposed method is a step forward in the development of cost-effective solutions for object tracking.