Authors:
Jeffrey Chan
1
;
Hector Carrión
2
;
Rémi Mégret
2
;
José L. Agosto Rivera
3
and
Tugrul Giray
3
Affiliations:
1
Department of Mathematics, University of Puerto Rico, Río Piedras Campus, Puerto Rico
;
2
Department of Computer Science, University of Puerto Rico, Río Piedras Campus, Puerto Rico
;
3
Department of Biology, University of Puerto Rico, Río Piedras Campus, Puerto Rico
Keyword(s):
Re-identification, Contrastive Learning, Self-supervised Learning, Animal Monitoring.
Abstract:
This paper presents an experimental study of long-term re-identification of honeybees from the appearance of their abdomen in videos. The first contribution is composed of two image datasets of single honeybees extracted from 12 days of video and annotated with information about their identity on long-term and short-term scales. The long-term dataset contains 8,962 images associated to 181 known identities and used to evaluate the long-term re-identification of individuals. The short-term dataset contains 109,654 images associated to 4,949 short-term tracks that provide multiple views of an individual suitable for self-supervised training. A deep convolutional network was trained to map an image of the honeybee’s abdomen to a 128 dimensional feature vector using several approaches. Re-identification was evaluated in test setups that capture different levels of difficulty: from the same hour to a different day. The results show using the short-term self-supervised information for trai
ning performed better than the supervised long-term dataset, with best performance achieved by using both. Ablation studies show the impact of the quantity of data used in training as well as the impact of augmentation, which will guide the design of future systems for individual identification.
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