Abstract
Simple SummaryCamera trap wildlife surveys can generate vast amounts of imagery. A key problem in the wildlife ecology field is that vast amounts of time is spent reviewing this imagery to identify the species detected. Valuable resources are wasted, and the scale of studies is limited by this review process. The use of computer software capable of extracting false positives, automatically identifying animals detected and sorting imagery could greatly increase efficiency. Artificial intelligence has been demonstrated as an effective option for automatically identifying species from camera trap imagery. Currently available code bases are inaccessible to the majority of users; requiring high-performance computers, advanced software engineering skills and, often, high-bandwidth internet connections to access cloud services. The ClassifyMe software tool is designed to address this gap and provides users the opportunity to utilise state-of-the-art image recognition algorithms without the need for specialised computer programming skills. ClassifyMe is especially designed for field researchers, allowing users to sweep through camera trap imagery using field computers instead of office-based workstations.We present ClassifyMe a software tool for the automated identification of animal species from camera trap images. ClassifyMe is intended to be used by ecologists both in the field and in the office. Users can download a pre-trained model specific to their location of interest and then upload the images from a camera trap to a laptop or workstation. ClassifyMe will identify animals and other objects (e.g., vehicles) in images, provide a report file with the most likely species detections, and automatically sort the images into sub-folders corresponding to these species categories. False Triggers (no visible object present) will also be filtered and sorted. Importantly, the ClassifyMe software operates on the user’s local machine (own laptop or workstation)—not via internet connection. This allows users access to state-of-the-art camera trap computer vision software in situ, rather than only in the office. The software also incurs minimal cost on the end-user as there is no need for expensive data uploads to cloud services. Furthermore, processing the images locally on the users’ end-device allows them data control and resolves privacy issues surrounding transfer and third-party access to users’ datasets.
Highlights
IntroductionPassive Infrared sensor activated cameras, otherwise known as camera traps, have proved to be a tool of major interest and benefit to wildlife management practitioners and ecological researchers [1,2].Camera traps are used for a diverse array of purposes including presence–absence studies [3,4,5], population estimates [6,7,8,9], animal behaviour studies [10,11,12], and species interactions studies [12,13,14].A comprehensive discussion of the applications of camera trap methodologies and applications are described in sources including [15,16,17]
The intent is that the user will upload an SD card of camera trap images, select relevant model and on thisondataset to automatically identify and sort thesort images the relevant model and run thenClassifyMe run ClassifyMe this dataset to automatically identify and the (Figure images1)
We will focus on the Australia (New England, NSW) model, further results of the other models are provided in Supplementary Material S3
Summary
Passive Infrared sensor activated cameras, otherwise known as camera traps, have proved to be a tool of major interest and benefit to wildlife management practitioners and ecological researchers [1,2].Camera traps are used for a diverse array of purposes including presence–absence studies [3,4,5], population estimates [6,7,8,9], animal behaviour studies [10,11,12], and species interactions studies [12,13,14].A comprehensive discussion of the applications of camera trap methodologies and applications are described in sources including [15,16,17]. The capacity of camera traps to collect large amounts of visual data provides an unprecedented opportunity for remote wildlife observation; these same datasets incur a large cost and burden as image processing can be time consuming [2,18]. The user is often required to inspect, identify and label tens-of-thousands of images per deployment, dependent on the number of camera traps deployed. Numerous software packages have been developed over the last 20 years to help with analysing camera trap image data [19], but these methods often require some form of manual image processing. Automation in image processing has been recognised internationally as a requirement for progress in wildlife monitoring [1,2] and this has become increasingly urgent as camera trap deployment has grown over time
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