Wildlife surveillance using deep learning methods

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.

UIOT-FMT: A Universal Format for Collection and Aggregation of Data from Smart Devices

“…Technologies that allows cameras to determine what entity lies within their field-of-view, has allowed for detection of human faces, objects and animals. In [ 25 ] it was shown that the use of machine learning techniques, a CCTV camera were capable of differentiating between different kinds of wildlife using Convolutional Neural Networks (CNN). Another example of entity awareness in CCTV systems were demonstrated in [ 26 ], where they demonstrated a way for cameras to search for faces, using cameras on a public transport system.…”

UIOT-FMT: A Universal Format for Collection and Aggregation of Data from Smart Devices

“…Technologies that allows cameras to determine what entity lies within their field-of-view, has allowed for detection of human faces, objects and animals. In [17] it was shown that the use of machine learning techniques, a CCTV camera were capable of differentiating between different kinds of wildlife using Convolutional…”

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

“…Similarly, the role of IoT, robotics, and computer vision techniques in various animal and pest monitoring applications has been extensively researched [ 18 , 19 , 20 , 21 ]. In Reference [ 22 ], Ruilong Chen et al developed an automated image classification algorithm to identify the wild animal badgers from domestic animals in farm buildings. Two deep learning-based image classification frameworks were used in this experiment.…”