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.

从静止图像或视频中对动物进行个体识别和重新识别对于动物行为，环境保护，生物学等方面的研究很有用。我们建议使用限制集分类（RSC）从同一张图片同时对多只动物进行分类。我们的文献综述表明，这个问题迄今尚未解决。我们使用卷积神经网络（CNN）作为个体分类器，将RSC应用于锦鲤视频。我们的结果表明，RSC优于仅应用CNN，因为它消除了同一图像中的重复标签，并提高了整体分类的准确性。