Recognition of Individual Zebrafish Using Speed-Up Robust Feature Matching
2017 10th International Conference on Developments in eSystems Engineering (DeSE)(2017)
摘要
Animals' tagging has been widely used to identify their individuality using physical methods. In small swimming animals (e.g. zebrafish), however, physical tagging is considered a painful, costly and impractical. This paper proposes a new tagging method for zebrafish that is based on Speed-Up Robust Feature (SURF) matching. In this method, a set of local features is extracted from a sequence of image frames collected through a computer vision system. The extracted set of features for each free-swimming fish is then compared with pre-extracted sets of features, stored in a database, using the SURF matching method. Feature vectors through SURF are formed by means of local patterns around key points, which are detected using a scaled-up filter. The performance of the proposed tagging method is assessed experimentally using six free-swimming zebrafish. The obtained results demonstrated an average accuracy of 90% which obtained with a matching-features threshold of 15%. These findings are promising towards developing a painless, cost-effective and practical animal tagging system for zebrafish.
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关键词
Animal tagging,computer vision,SURF,zebrafish
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