Past, present and future approaches using computer vision for animal re‐identification from camera trap data. Issue 4 (20th January 2019)

• Record Type: Journal Article
• Authors: Schneider, Stefan; Taylor, Graham W.; Linquist, Stefan; Kremer, Stefan C.
• Editors: O'Hara, Robert B.
• Is Part Of: Methods in ecology and evolution. Volume 10:Issue 4(2019)
• Page Start: 461
• Page End: 470
• Publication Date: 2019-01-20
• Subjects: animal reidentification -- camera traps -- computer vision -- convolutional networks -- deep learning -- density estimation -- monitoring -- object detection; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13133
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: The ability of a researcher to re‐identify (re‐ID) an individual animal upon re‐encounter is fundamental for addressing a broad range of questions in the study of ecosystem function, community and population dynamics and behavioural ecology. Tagging animals during mark and recapture studies is the most common method for reliable animal re‐ID; however, camera traps are a desirable alternative, requiring less labour, much less intrusion and prolonged and continuous monitoring into an environment. Despite these advantages, the analyses of camera traps and video for re‐ID by humans are criticized for their biases related to human judgement and inconsistencies between analyses. In this review, we describe a brief history of camera traps for re‐ID, present a collection of computer vision feature engineering methodologies previously used for animal re‐ID, provide an introduction to the underlying mechanisms of deep learning relevant to animal re‐ID, highlight the success of deep learning methods for human re‐ID, describe the few ecological studies currently utilizing deep learning for camera trap analyses and our predictions for near future methodologies based on the rapid development of deep learning methods. For decades, ecologists with expertise in computer vision have successfully utilized feature engineering to extract meaningful features from camera trap images to improve the statistical rigor of individual comparisons and remove human bias from their camera trap analyses. Recent years have witnessed the emergence of deep learning systems which have demonstrated the accurate re‐ID of humans based on image and video data with near perfect accuracy. Despite this success, ecologists have yet to utilize these approaches for animal re‐ID. By utilizing novel deep learning methods for object detection and similarity comparisons, ecologists can extract animals from an image/video data and train deep learning classifiers to re‐ID animal individuals beyond the capabilities of a human observer. This methodology will allow ecologists with camera/video trap data to reidentify individuals that exit and re‐enter the camera frame. Our expectation is that this is just the beginning of a major trend that could stand to revolutionize the analysis of camera trap data and, ultimately, our approach to animal ecology.