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Nowadays, video recordings of sport events is standard practice for a variety of applications, ranging from entertainment to competition analysis. Beside that, analysis of athletes while exercising is of particular interest for their coaches in order to gain insight into training quality and allow for training control. To bring together video recordings and the desire for analysis we present the implementation of a genetic algorithm (GA) for the important step of camera calibration. Our implementation can be used not only in a prospective but also in a retrospective manner for the squash sport. We do not rely on directly or manually provided image-world coordinates, but rather only on the playing field as known geometric object, present in the physical camera&#x2019;s captured image. To find the best GA configuration, we evaluate all combinations of 2 initialization-, 2 fitness-, 3 selection-, 4 crossover-, and 2 mutation strategies. We apply and evaluate the GA&#x2019;s accuracy on synthetic, artificial renderings, and real world data as well as comparing it to other standard optimization algorithms. Our results reveal the importance of correct camera placement and show sufficient accuracy for our goal of athlete movement analysis. The results will serve for a automatic athlete movement analysis tool to support squash specific training procedures.

Evolution Based Single Camera Resectioning Based on Distance Maps of a Known Geometry for Squash Sports