Ball localization and tracking in a highly dynamic table soccer environment |
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| Authors: | Rob Janssen Mark Verrijt Jeroen de Best René van de Molengraft |
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| Affiliation: | 1. Florida International University, Miami, FL 33174, United States;2. Illinois Institute of Technology, Chicago, IL 60616, United States;1. Data Mining and Optimisation Research Group, Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;2. Faculty of Information & Communication Technologies, Swinburne University of Technology, Victoria 3122, Australia;1. Shenzhen Key Lab of Advanced Communications and Information Processing, Shenzhen University, Shenzhen 518060, China;2. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;1. School of Physics and Key Laboratory of MEMS of the Ministry of Education, Southeast University, Nanjing 211189, China;2. Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA;3. Hyper Tech Research Incorporated, 539 Industrial Mile Road, Columbus, OH 43228, USA |
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| Abstract: | This article presents the development of a ball localization and tracking algorithm, that is to be applied in a highly dynamic table soccer environment. The described approach is based on an earlier survey paper on object tracking, where a general selection procedure on object detection and tracking techniques was proposed. Although the survey paper presents a variety of state estimation techniques for tracking, this article describes why these are not well suited for our specific application. For this reason, an IMM estimation technique is adopted that has not been applied in this highly dynamic context before. To evaluate the IMM estimator, it is compared to the well-known and commonly used Kalman filter, that has been optimally tuned for this specific application. |
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