Using ORB, BoW and SVM to identify and track tagged Norway lobster Nephrops norvegicus (L.)
Document typeConference lecture
Rights accessOpen Access
Sustainable capture policies of many species strongly depend on the understanding of their social behaviour. Nevertheless, the analysis of emergent behaviour in marine species poses several challenges. Usually animals are captured and observed in tanks, and their behaviour is inferred from their dynamics and interactions. Therefore, researchers must deal with thousands of hours of video data. Without loss of generality, this paper proposes a computer vision approach to identify and track specific species, the Norway lobster, Nephrops norvegicus. We propose an identification scheme were animals are marked using black and white tags with a geometric shape in the center (holed triangle, filled triangle, holed circle and filled circle). Using a massive labelled dataset; we extract local features based on the ORB descriptor. These features are a posteriori clustered, and we construct a Bag of Visual Words feature vector per animal. This approximation yields us invariance to rotation and translation. A SVM classifier achieves generalization results above 99%. In a second contribution, we will make the code and training data publically available.
CitationGarcía del Arco, José Antonio [et al.]. Using ORB, BoW and SVM to identify and track tagged Norway lobster Nephrops norvegicus (L.). A: 7th International Workshop on Marine Technology : MARTECH 2016. "Instrumentation viewpoint". Vilanova i la Geltrú: SARTI, 2016, p. 50-52.