The app uses state of the art computer vision techniques to identify images that contain species of wildlife of interest to trailcam users while also filtering out images that do not contain any wildlife.

Common Problems With Trailcams

One common problem with trailcams is that they often take photos when the wind causes trees or grass to move. Or when shadows shift due to the sun movement. Or even as the sun warms up vegetation, rocks, pavement in the camera's field of view.

Consider the two images below taken 17 minutes apart. The trailcam that took these photos took another 12 photos in between these two as the shadow from trees moved during that time.

Trailcam image with no wildlifeAnother trailcam image with no wildlife
Two images from a sequence of images showing how moving shadows often cause trailcams to take photos when there is no wildlife present

None of the 14 photos that were taken contained wildlife. The app helps you identify only images that contain actual wildlife by filtering out images like these. It's not unusual to have 75-90% of trailcam images that contain no wildlife. The app saves you time and headaches sorting through your trailcam images so you can see just the images that are of interest to you.

Analyzes Your Photos

The app uses computer vision to determine which photos contain actual wildlife and which photos do not. So instead of clicking or swiping through image after image of trees blowing in the wind the app allows you to see just the photos that actually have wildlife in them.

Counts Points Automatically

For photos that contain a buck the app also estimates how many points are on the buck's antlers. If a photo contains two or more bucks the app reports the number of points for the buck with the most points.

You can then view, say, images of bucks that contain 8 or more points to help you find photos of deer you are interested in. Combine this with the app's ability to filter images based on location and time-of-day and you can quickly determine what trailcam has the most big bucks going by it in the morning (for example).