TY - JOUR

T1 - Animal Reidentification using Restricted Set Classification

AU - Kuncheva, Ludmila

PY - 2021/5

Y1 - 2021/5

N2 - Individual animal recognition and re-identification from still images or video are useful for research in animal behaviour, environment preservation, biology and more. We propose to use Restricted Set Classification (RSC) for classifying multiple animals simultaneously from the same image. Our literature review revealed that this problem has not been solved thus far. We applied RSC on a koi fish video using a convolutional neural network (CNN) as the individual classifier. Our results demonstrate that RSC is significantly better than applying just the CNN, as it eliminates duplicate labels in the same image and improves the overall classification accuracy.

AB - Individual animal recognition and re-identification from still images or video are useful for research in animal behaviour, environment preservation, biology and more. We propose to use Restricted Set Classification (RSC) for classifying multiple animals simultaneously from the same image. Our literature review revealed that this problem has not been solved thus far. We applied RSC on a koi fish video using a convolutional neural network (CNN) as the individual classifier. Our results demonstrate that RSC is significantly better than applying just the CNN, as it eliminates duplicate labels in the same image and improves the overall classification accuracy.

U2 - 10.1016/j.ecoinf.2021.101225

DO - 10.1016/j.ecoinf.2021.101225

M3 - Article

VL - 62

JO - Ecological Informatics

JF - Ecological Informatics

SN - 1574-9541

M1 - 101225

ER -