Sherlock - A flexible, low-resource tool for processing camera-trapping images

Abstract

1O_LIThe use of camera traps to study wildlife has increased markedly in the last two decades. Camera surveys typically produce large datasets which require processing to isolate images containing the species of interest. This is time consuming and costly, particularly if there are many empty images that can result from false triggers. Computer vision technology can assist with data processing, but existing artificial intelligence algorithms are limited by the requirement of a training dataset, which itself can be challenging to acquire. Furthermore, deep-learning methods often require powerful hardware and proficient coding skills. C_LIO_LIWe present Sherlock, a novel algorithm that can reduce the time required to process camera trap data by removing a large number of unwanted images. The code is adaptable, simple to use and requires minimal processing power. C_LIO_LIWe tested Sherlock on 240 596 camera trap images collected from 46 cameras placed in a range of habitats on farms in Cornwall, UK, and set the parameters to find European badgers (Meles meles). The algorithm correctly classified 91.9% of badger images and removed 49.3% of the unwanted empty images. When testing model parameters, we found that faster processing times were achieved by reducing both the number of sampled pixels and bouncing attempts (the number of paths explored to identify a disturbance), with minimal implications for model sensitivity and specificity. When Sherlock was tested on two sites which contained no livestock in their images, its performance greatly improved and it removed 92.3% of the empty images. C_LIO_LIThough further refinements may improve its performance, Sherlock is currently an accessible, simple and useful tool for processing camera trap data. C_LI