Utility of machine learning for segmenting camera trap time‐lapse recordings

Abstract

AbstractCamera trap time‐lapse recordings can collect vast amounts of data on wildlife in their natural settings. Transforming these data into information useful to ecologists is a major challenge. Machine learning techniques show promise for becoming important tools in the cost‐effective analysis of camera trap data, but only if they become readily available to researchers without requiring advanced computing skills and resources. We present a new suite of software tools that reduce the amount of human effort needed to segment time‐lapse, camera trap recordings in preparation for analysis. The tools incorporate a convolutional neural network trained to detect a focal species and to generate a draft video segmentation indicating the ranges of time when the focal species is present. We evaluated the utility of our neural network by comparing manual and automatic segmentations of 64 time‐lapse recordings of gopher tortoise (Gopherus polyphemus) burrows, recorded in Pinellas County, Florida, USA between 25 November 2020 and 30 November 2020. The neural network correctly found 130 of the 145 segments containing tortoises (89.7%), whereas student graders found 135 segments (93.1%). A year of experience using the new software suite in an ongoing study of gopher tortoises deploying 12 camera traps indicates one person, assisted by machine learning algorithms, can segment a week's worth of time‐lapse recordings—11.5 hours of standard‐speed video—in under 3 hours. We concluded that the use of machine learning algorithms is practical and allows researchers to process large volumes of time‐lapse data with minimal human effort.