Identification of previously unseen Asian elephants using visual data and semi-supervised learning

This paper presents a novel method to identify unseen Asian elephants that are not previously captured or identified in available data sets and re-identify previously seen Asian elephants using images of elephant ears, leveraging a semi-supervised learning approach. Ear patterns of unseen elephants are learnt for future re-identification. To aid our process, elephant ear patterns are used as a biomarker to uniquely identify individual Asian elephant, each of which is attached a descriptor. The main challenge is to learn and use a clustering technique to identify new classes (i.e., elephants) in unlabelled elephant ear image sets and leveraging this data in verifying the labelled images. This study proposes a systematic approach to address the problem to uniquely identify elephants, where we developed: (a) a self-supervised learning approach for training the representation of labelled and unlabelled image data to avoid unWanted, bias labelled data, (b) rank statistics for transferring the models’ knowledge of the labelled classes when clustering the unlabelled images, and, (c) improving the identification accuracy of both the classification and clustering algorithms by introducing a optimization problem when training with the data representation on the labelled and unlabelled image data sets. This approach was evaluated on seen (labelled) and unseen (unlabelled) elephants, where we achieved a significant accuracy of 86.89% with an NMI (Normalized Mutual Information) score of 0.9132 on identifying seen elephants. Similarly, an accuracy of 54.29% with an NMI score of 0.6250 was achieved on identifying unseen elephants from the unlabelled Asian elephant ear image data set. Findings of this research provides the ability to accurately identify elephants without having expert knowledge on the field. Our method can be used to uniquely identify elephants from their herds and then use it to track their travel patterns Which is greatly applicable in understanding the social organization of elephant herds, individual behavioural patterns, and estimating demographic parameters as a measure to reducing the human-elephant conflict in Sri Lanka

Identificación de Ratas de Laboratorio mediante Visión Artificial y Aprendizaje Máquina

[Resumen]: En la actualidad, la experimentación animal continúa vigente: en la investigación y monitorización del comportamiento, la farmacología y la ecotoxicología, en los estudios de los efectos del cambio climático, en los estudios de procesos de aprendizaje, así como en la monitorización de granjas. Considerando necesario e importante acompañar la investigación mediante la experimentación animal, es relevante mencionar los programas de seguimiento de imágenes. Mediante la determinación de la posición de los animales, cada imagen, y relacionando las posiciones del animal a lo largo del tiempo para generar su trayectoria, podemos extraer información de gran utilidad. El problema reside en relacionar cada posición con el animal que le corresponde cuando estudiamos múltiples individuos. Los momentos críticos son los instantes de oclusión entre individuos o con el entorno. En este proyecto estudiaré y validaré las de técnicas de aprendizaje máquina y transferencia del conocimiento para el estudio de ratas de laboratorio en imágenes. El aprendizaje máquina son un grupo de técnicas capaces de aprender relaciones entre datos. La metodología de la transferencia del conocimiento se basa en la reutilización de técnicas previamente entrenadas en tareas similares. Mediante la velocidad del entrenamiento del aprendizaje por transferencia, en contraposición con enfoques tradicionales de aprendizaje máquina, estas técnicas suponen una optimización sustancial en el tiempo de entrenamiento de los modelos. Así, el resultado fundamental de este trabajo, es demostrar la viabilidad de las técnicas de aprendizaje máquina con transferencia del conocimiento. para identificar animales de laboratorio como las ratas en videos de ensayos animales.[Abstract]: Today, animal experimentation continues: in behavioural research and monitoring, pharmacology and ecotoxicology, in studies of the effects of climate change, in studies of learning processes, as well as in farm monitoring. Considering it necessary and important to accompany research through animal experimentation, it is relevant to mention image-tracking programs. By determining the position of the animals in each image and relating the animal's positions over time to generate its trajectory, we can extract useful information. The problem arises when relating each position to the corresponding animal when we study multiple individuals. The critical moments occur in the moments of occlusion between individuals or with the environment. In this research, I will study and validate machine learning and knowledge transfer techniques for the study of laboratory rats in images. Machine learning is a group of techniques capable of learning relationships between data. The knowledge transfer methodology based on the reuse of techniques previously trained in similar tasks. Through the speed of transfer learning training, as opposed to traditional machine learning approaches, these techniques provide substantial optimization in model training time. Thus, the fundamental result of this work is to demonstrate the feasibility of machine learning techniques with knowledge transfer to identify laboratory animals such as rats in animal testing videos.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202