MotionMeerkat: integrating motion video detection and ecological monitoring

Abstract

Summary1. Human observation is expensive and limits the breadth of data collection. For this reason, remotely placed video cameras are increasingly used to monitor animals. One drawback of field-based video recordings is extensive review time. Computer vision can mitigate this cost and enhance data collection by extracting biological information from images with minimal time investment. 2. MotionMeerkat is a new standalone program that identifies motion events from a video stream. After running a video, the user reviews a folder of candidate motion frames for the target organism. This tool reduces the time needed to review videos and accommodates a variety of inputs. 3. I tested MotionMeerkat using hummingbird-plant videos recorded in a tropical montane forest. To validate the optimal parameter set for finding motion events, I counted hummingbirds observed from direct video review compared to events found in images returned from MotionMeerkat. To show the generality of the approach, MotionMeerkat was tested on a set of terrestrial and underwater videos. To assess the performance of the background subtraction for further image analysis, I hand counted the number of frames with target organisms and compared them to the MotionMeerkat output. 4. MotionMeerkat was highly successful in finding motion events and often reduced the number of frames needed to capture hummingbird visitation by over 90%. Both background approaches effectively found a variety of organisms in ecological videos. I provide general recommendations for parameter settings and extending this approach in the future.

Wildlife speed cameras: measuring animal travel speed and day range using camera traps

“…Various approaches requiring little or no additional fieldwork are theoretically possible to achieve this, and we are currently working to explore these and develop the necessary tools. Image analysis can potentially be further automated by extraction of the animal silhouette from pixel differences between subsequent images (Ren et al 2013;Weinstein 2015), and ultimately even automated identification of species (Yu et al 2013;Swinnen et al 2014).…”

A computer vision for animal ecology

“…Even motion triggered camera traps suffer from many false‐positive images due to wind and moving vegetation. In computer vision, finding novel objects within series of images can be achieved using background subtraction, which distinguishes sedentary objects, such as trees and clouds, from moving objects, such as animals, within videos or groups of images (Price Tack et al., ; Ren, Han, & He, ; Weinstein, ) (Figure a). A background model is created by computing an expected image based on the previous pixel values (Stauffer & Grimson, ).…”

“…Applications of computer vision to detecting and counting ecological objects. (a) Background subtraction of video frames yields the desired motion object (Weinstein, ) based on changes in past pixel values. (b) Counting wildebeest from imagery captured by unmanned aerial vehicle in Tanzania (Torney et al., ).…”

“…To determine the number of visits between birds and flowers, Weinstein and Graham () used time‐lapse cameras to film multiple days of flower visitation. Using background subtraction algorithms, they were able to process over 8,000 hr of hummingbird visitation videos (Weinstein, ). This video‐based strategy allowed sampling at much greater temporal extents, and therefore minimized the potential for overlooking rare interactions.…”

Plant and habitat use by Black-breasted Pufflegs (Eriocnemis nigrivestis), a critically endangered hummingbird

“…Videos were analyzed with Motion Meerkat software (v 1.5; Weinstein ). Motion Meerkat is motion‐detection software that selects motion‐candidate frames from streaming videos.…”