Abstract
Wildlife conservation and the management of human–wildlife conflicts require cost‐effective methods of monitoring wild animal behavior. Still and video camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. In the present study, we describe a state‐of‐the‐art, deep learning approach for automatically identifying and isolating species‐specific activity from still images and video data.We used a dataset consisting of 8,368 images of wild and domestic animals in farm buildings, and we developed an approach firstly to distinguish badgers from other species (binary classification) and secondly to distinguish each of six animal species (multiclassification). We focused on binary classification of badgers first because such a tool would be relevant to efforts to manage Mycobacterium bovis (the cause of bovine tuberculosis) transmission between badgers and cattle.We used two deep learning frameworks for automatic image recognition. They achieved high accuracies, in the order of 98.05% for binary classification and 90.32% for multiclassification. Based on the deep learning framework, a detection process was also developed for identifying animals of interest in video footage, which to our knowledge is the first application for this purpose.The algorithms developed here have wide applications in wildlife monitoring where large quantities of visual data require screening for certain species.
Highlights
The use of remote still and video surveillance cameras in wildlife re‐ search and management has grown rapidly in recent years (Nguyen et al, 2017; Villa, Salazar, & Vargas, 2017; Zeppelzauer, 2013)
We used a dataset consisting of 8,368 images of wild and domestic animals in farm buildings, and we developed an approach firstly to distinguish badgers from other species and secondly to distinguish each of six animal species
We focused on binary classification of badgers first because such a tool would be relevant to efforts to manage Mycobacterium bovis transmission between badgers and cattle
Summary
The use of remote still and video surveillance cameras in wildlife re‐ search and management has grown rapidly in recent years (Nguyen et al, 2017; Villa, Salazar, & Vargas, 2017; Zeppelzauer, 2013). Despite being motion‐ triggered, both approaches produce a large amount of visual data that need to be manually reviewed for target and nontarget species To address these challenges, we piloted the use of machine learn‐ ing methods for automatic recognition of wildlife. Very recently, Norouzzadeh et al (2018) applied different CNN architectures including AlexNet (Krizhevsky et al, 2012), VGG (Simonyan & Zisserman, 2015), and ResNet (He, Zhang, Ren, & Sun, 2016) to the same dataset and achieved an accuracy of 92% for species identification While these methods show improved accuracy, we are not aware of any studies that have considered how to detect wildlife images of interest from film sequences. We aim to develop a robust framework to classify wildlife im‐ ages, and we apply the same image recognition algorithm to video footage
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