1. 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.