Soccer Ball Detection by Comparing Different Feature Extraction Methodologies
Author(s) -
Pier Luigi Mazzeo,
Marco Leo,
Paolo Spagnolo,
Massimiliano Nitti
Publication year - 2012
Publication title -
advances in artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 1687-7489
pISSN - 1687-7470
DOI - 10.1155/2012/512159
Subject(s) - computer science , robustness (evolution) , feature extraction , ball (mathematics) , artificial intelligence , probabilistic logic , pattern recognition (psychology) , data mining , machine learning , mathematics , mathematical analysis , biochemistry , chemistry , gene
This paper presents a comparison of different feature extraction methods for automatically recognizing soccer ball patterns through a probabilistic analysis. It contributes to investigate different well-known feature extraction approaches applied in a soccer environment, in order to measure robustness accuracy and detection performances. This work, evaluating different methodologies, permits to select the one which achieves best performances in terms of detection rate andCPU processing time. The effectiveness of the different methodologies is demonstrated by a huge number of experiments on real ball examples under challengingconditions
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