An active learning model to classify animal species in Hong Kong

2024/03/23 by Gareth Lamb, Lamb, Gareth, Ching Hei Lo +5
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Environmental Science · #Animal Disease Management and Epidemiology #Artificial intelligence #Business #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #Geography #Identification and Quantification in Food #Machine learning #Species Distribution and Climate Change

paper · pdf · doi:10.48550/arxiv.2403.15675

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/03/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it possible to automating this process [1]. A major obstacle to this is the generalisability of these models when applying these images to independently collected data from other parts of the world [2]. Here, we use a deep active learning workflow [3], and train a model that is applicable to camera trap images collected in Hong Kong.

Related

• A deep active learning system for species identification and counting in camera trap images
• Efficient Pipeline for Camera Trap Image Review
• The iWildCam 2020 Competition Dataset
• Application of deep learning to camera trap data for ecologists in planning / engineering -- Can captivity imagery train a model which generalises to the wild?
• A model-agnostic active learning approach for animal detection from camera traps