FoxMask: a new automated tool for animal detection in camera trap images

Camera traps now represent a reliable, efficient and cost-effective technique to monitor wildlife and collect biological data in the field. However, efficiently extracting information from the massive amount of images generated is often extremely time-consuming and may now represent the most rate-limiting step in camera trap studies. To help overcome this challenge, we developed FoxMask, a new tool performing the automatic detection of animal presence in short sequences of camera trap images. FoxMask uses background estimation and foreground segmentation algorithms to detect the presence of moving objects (most likely, animals) on images. We analyzed a sample dataset from camera traps used to monitor activity on arctic fox Vulpes lagopus dens to test the parameter settings and the performance of the algorithm. The shape and color of arctic foxes, their background at snowmelt and during the summer growing season were highly variable, thus offering challenging testing conditions. We compared the automated animal detection performed by FoxMask to a manual review of the image series. The performance analysis indicated that the proportion of images correctly classified by FoxMask as containing an animal or not was very high (> 90%). FoxMask is thus highly efficient at reducing the workload by eliminating most false triggers (images without an animal). We provide parameter recommendations to facilitate usage and we present the cases where the algorithm performs less efficiently to stimulate further development. FoxMask is an easy-to-use tool freely available to ecologists performing camera trap data extraction. By minimizing analytical time, computer-assisted image analysis will allow collection of increased sample sizes and testing of new biological questions.