The present article proposes the training, testing and comparison of two models for ball detection, taking into account its final implementation in a Boccia game analysis computer-vision algorithm, within the 'iBoccia' framework. The goal is to have a versatile and flexible algorithm towards different game environments. The selected ball detectors were a Histogram-of-Oriented-Gradients feature based Support Vector Machine (HOG-SVM) and a Convolutional Neural Network (CNN) based on a less complex implementation of the You Only Look Once model (Tiny-YOLO). Both detectors were evaluated offline and in real-time. The subsequent results showed that their performance was similar in both evaluations, however, Tiny-YOLO outperformed HOG-SVM by a small margin in all the used metrics. In real-time, both detectors achieved an accuracy of approximately 90%. Despite the high accuracy values, the detector requires further improvement because a single non-detection can influence the computer-vision algorithm's output, making the system unreliable. ; FCT - Fundação para a Ciência e a Tecnologia(SFRH/BD/ SFRH/BD/133314/2017) ; This article is supported by the project Deus ex Machina: NORTE – 01 – 0145 – FEDER - 000026, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Vinicius Silva also thanks FCT for the PhD scholarship SFRH/BD/ SFRH/BD/133314/2017.
Ball detection for boccia game analysis
2019-01-01
Conference paper
Electronic Resource
English
DDC: | 629 |
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