A neural network approach to camera calibration for movement tracking

Authors

  • Bernard Alan Racoma National Institute of Physics, University of the Philippines Diliman
  • Maricor N. Soriano National Institute of Physics, University of the Philippines Diliman

Abstract

In this paper, neural networks were trained to obtain the transform between pixel coordinates and real world coordinates. We determined that it is possible to calibrate using neural networks utilizing a combination of points that both lie near the center and around the edges of an area of interest. A minimal number of 33 input and output pairs can be used to successfully calibrate a playing field with an average Euclidean distance error of 1.28 cm. We demonstrated our technique by calibrating a Foosball table using 23 points. We tracked a ball moving around the table and then using the transform learned by the network, we were able to obtain the actual coordinates of the trajectory.

Downloads

Issue

Article ID

SPP-2010-PA-08

Section

Poster Session PA

Published

2010-10-25

How to Cite

[1]
BA Racoma and MN Soriano, A neural network approach to camera calibration for movement tracking, Proceedings of the Samahang Pisika ng Pilipinas 28, SPP-2010-PA-08 (2010). URL: https://proceedings.spp-online.org/article/view/SPP-2010-PA-08.