A Robust Sphere Detection in a Realsense Point Cloud by USING Z-Score and RANSAC

Abstract

Three-dimensional vision cameras, such as RGB-D, use 3D point cloud to represent scenes. File formats as XYZ and PLY are commonly used to store 3D point information as raw data, this information does not contain further details, such as metadata or segmentation, for the different objects in the scene. Moreover, objects in the scene can be recognized in a posterior process and can be used for other purposes, such as camera calibration or scene segmentation. We are proposing a method to recognize a basketball in the scene using its known dimensions to fit a sphere formula. In the proposed cost function we search for three different points in the scene using RANSAC (Random Sample Consensus). Furthermore, taking into account the fixed basketball size, our method differentiates the sphere geometry from other objects in the scene, making our method robust in complex scenes. In a posterior step, the sphere center is fitted using z-score values eliminating outliers from the sphere. Results show our methodology converges in finding the basketball in the scene and the center precision improves using z-score, the proposed method obtains a significant improvement by reducing outliers in scenes with noise from 1.75 to 8.3 times when using RANSAC alone. Experiments show our method has advantages when comparing with novel deep learning method.

Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching

“…Zhang et al [13] utilize Harris corner detection, SURF feature extraction, and a particle swarm optimization algorithm to improve basketball tracking results. Roman et al [14] preset a fixed basketball size and search for basketball position points in the scene using RANSAC (Random Sample Consensus). In a posterior step, the sphere center is fitted using z-score values eliminating outliers from the sphere.…”