Efficacy of Automated Detection of Motion in Wildlife Monitoring Videos

Documenting biodiversity and wildlife behavior is time-consuming and potentially invasive. Remotely placed cameras often are used to increase sampling and minimize disturbance of focal animals. Such wildlife monitoring programs can entail numerous and lengthy videos requiring massive amounts of time to analyze their content. We evaluated the efficacy of a computer program, MotionMeerkat (http://benweinstein.weebly.com/motionmeerkat.html), for automated detection of motion in 21 continuous, 3-hour-long videos monitoring woodpecker (Picidae) nest cavities during 2016 in Washington, USA. The program produces still-image frame captures from the video where motion was detected, files logging the video time-stamps of each frame, and x,y coordinates within each frame where motion was detected. We compared program results with video reviews conducted by students using a standard protocol for identifying movement events and species identification. The program tagged 60% more motion events of all types, and 38% more motion events of target species (woodpeckers and potential predators), than were found by student reviews. We also conducted sensitivity analyses of the program by varying program input settings; results suggest that optimal settings depend on the video quality, environmental conditions, desired target species, and type of motion. We conclude that automated motion-detection programs such as MotionMeerkat can play important roles in use of continuous videos for wildlife monitoring, but results need to be reviewed by the researcher.