Sports Camera Calibration via Synthetic Data

Calibrating sports cameras is important for autonomous broadcasting and sports analysis. Here we propose a highly automatic method for calibrating sports cameras from a single image using synthetic data. First, we develop a novel camera pose engine. The camera pose engine has only three significant free parameters so that it can effectively generate a lot of camera poses and corresponding edge (i.e, field marking) images. Then, we learn compact deep features via a siamese network from paired edge image and camera pose and build a feature-pose database. After that, we use a novel two-GAN (generative adversarial network) model to detect field markings in real images. Finally, we query an initial camera pose from the feature-pose database and refine camera poses using truncated distance images. We evaluate our method on both synthetic and real data. Our method not only demonstrates the robustness on the synthetic data but also achieves the state-of-the-art accuracy on a standard soccer dataset and very high performance on a volleyball dataset.Comment: 6 + 1 page

Estimating Metric Poses of Dynamic Objects Using Monocular Visual-Inertial Fusion

A monocular 3D object tracking system generally has only up-to-scale pose estimation results without any prior knowledge of the tracked object. In this paper, we propose a novel idea to recover the metric scale of an arbitrary dynamic object by optimizing the trajectory of the objects in the world frame, without motion assumptions. By introducing an additional constraint in the time domain, our monocular visual-inertial tracking system can obtain continuous six degree of freedom (6-DoF) pose estimation without scale ambiguity. Our method requires neither fixed multi-camera nor depth sensor settings for scale observability, instead, the IMU inside the monocular sensing suite provides scale information for both camera itself and the tracked object. We build the proposed system on top of our monocular visual-inertial system (VINS) to obtain accurate state estimation of the monocular camera in the world frame. The whole system consists of a 2D object tracker, an object region-based visual bundle adjustment (BA), VINS and a correlation analysis-based metric scale estimator. Experimental comparisons with ground truth demonstrate the tracking accuracy of our 3D tracking performance while a mobile augmented reality (AR) demo shows the feasibility of potential applications.Comment: IROS 201

Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM

The monocular visual-inertial system (VINS), which consists one camera and one low-cost inertial measurement unit (IMU), is a popular approach to achieve accurate 6-DOF state estimation. However, such locally accurate visual-inertial odometry is prone to drift and cannot provide absolute pose estimation. Leveraging history information to relocalize and correct drift has become a hot topic. In this paper, we propose a monocular visual-inertial SLAM system, which can relocalize camera and get the absolute pose in a previous-built map. Then 4-DOF pose graph optimization is performed to correct drifts and achieve global consistent. The 4-DOF contains x, y, z, and yaw angle, which is the actual drifted direction in the visual-inertial system. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. Current map and previous map can be merged together by the global pose graph optimization. We validate the accuracy of our system on public datasets and compare against other state-of-the-art algorithms. We also evaluate the map merging ability of our system in the large-scale outdoor environment. The source code of map reuse is integrated into our public code, VINS-Mono.Comment: 8 page