122 research outputs found
Automated Visual Fin Identification of Individual Great White Sharks
This paper discusses the automated visual identification of individual great
white sharks from dorsal fin imagery. We propose a computer vision photo ID
system and report recognition results over a database of thousands of
unconstrained fin images. To the best of our knowledge this line of work
establishes the first fully automated contour-based visual ID system in the
field of animal biometrics. The approach put forward appreciates shark fins as
textureless, flexible and partially occluded objects with an individually
characteristic shape. In order to recover animal identities from an image we
first introduce an open contour stroke model, which extends multi-scale region
segmentation to achieve robust fin detection. Secondly, we show that
combinatorial, scale-space selective fingerprinting can successfully encode fin
individuality. We then measure the species-specific distribution of visual
individuality along the fin contour via an embedding into a global `fin space'.
Exploiting this domain, we finally propose a non-linear model for individual
animal recognition and combine all approaches into a fine-grained
multi-instance framework. We provide a system evaluation, compare results to
prior work, and report performance and properties in detail.Comment: 17 pages, 16 figures. To be published in IJCV. Article replaced to
update first author contact details and to correct a Figure reference on page
A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments
We put forward a video dataset with 5k+ facial bounding box annotations
across a troop of 7 western lowland gorillas at Bristol Zoo Gardens. Training
on this dataset, we implement and evaluate a standard deep learning pipeline on
the task of facially recognising individual gorillas in a zoo environment. We
show that a basic YOLOv3-powered application is able to perform identifications
at 92% mAP when utilising single frames only. Tracking-by-detection-association
and identity voting across short tracklets yields an improved robust
performance of 97% mAP. To facilitate easy utilisation for enriching the
research capabilities of zoo environments, we publish the code, video dataset,
weights, and ground-truth annotations at data.bris.ac.uk
Visual Recognition of Great Ape Behaviours in the Wild
We propose a first great ape-specific visual behaviour recognition system
utilising deep learning that is capable of detecting nine core ape behaviours.Comment: 4 pages, 4 figures, to be published in the proceedings of ICPR 2020
at the Visual observation and analysis of Vertebrate And Insect Behaviour
(VAIB) worksho
Fusing Animal Biometrics with Autonomous Robotics::Drone-based Search and Individual ID of Friesian Cattle [Extended Abstract]
Back to the Future:Cycle Encoding Prediction for Self-supervised Video Representation Learning
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