Analyzing animal tracks in snow using computer vision

Snow, like other natural surfaces, can create a record of wildlife activity. For instance, footprints left behind in the snow can showcase which animals passed through, how they moved, and what they were doing.

For decades, hikers, hunters, and researchers have studied these prints to learn more about wild animal behavior. But the process is not always reliable. Snow can shift, the wind can blur details, and overlapping tracks can make identification difficult. Even trained observers may overlook important patterns.

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Technological advances are now making it easier to interpret these signs. In particular, computer vision, a branch of AI that allows machines to analyze visual data with accuracy and speed, can be used to detect and understand animal tracks. For instance, models like Ultralytics YOLO11 can be trained to detect shapes and patterns in images of animal tracks.

In this article, we’ll explore how animal tracks in snow are preserved, what they reveal about wildlife, and how computer vision is making tracking more efficient.

The importance of animal tracks in the snow

Animal tracks are impressions left as animals move through surfaces such as snow, soil, or mud. In the right snow conditions, these prints often preserve sharper details, like claw marks, toe pads, and the differences between front and hind feet, that are harder to detect in dirt or grass. 

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Beyond identifying species, the spacing, arrangement, and subtle variations in tracks can tell researchers a lot about movement, behavior, and interactions with the environment, providing researchers, hikers, and wildlife enthusiasts with valuable insights into animal activity.

Here are some of the key features that researchers look for while reading tracks:

• Front vs. hind feet: The size and shape differences between front paws and back feet reflect how an animal distributes its weight and moves through its environment.
  
• Paw prints or animal prints: The overall outline and size of a print help identify which species made the track.
  
• Claw marks: Canine tracks, such as those of coyotes, foxes, and dogs, usually show claw impressions, while feline tracks, including bobcats, domestic or house cats, and mountain lions, typically do not unless the animal is running or moving on slippery ground.
  
• Track pattern: The arrangement of tracks can reveal animal behavior, with coyotes and foxes often leaving straight-line trails, while domestic dogs tend to wander in a zigzag path.‍
• Straddle and draglines: The width between left and right tracks (straddle) varies by species and gait, and changes in straddle can suggest movement speed or caution, while draglines from tails, bellies, or prey provide additional clues about activity.

Identifying animal tracks in the snow 

Every track in the snow tells part of an animal’s story. The size and shape of each print, the differences between front and hind feet, and the presence or absence of claw marks can reveal species, gait, and weight distribution. For example, foxes and coyotes often leave visible claw impressions, while bobcats and mountain lions usually do not.

Tracks rarely appear on their own. Clues like scat, bits of fur, tail drags, or nearby den entrances often add important context. Since snow conditions and overlapping trails can blur details, trackers rely on several signs together to build a clearer picture. The way tracks are arranged on the snow is especially helpful, highlighting not just where an animal went, but how it was moving and behaving.

Here are some common animal track patterns found in snow:

• Hoppers: Rabbits and snowshoe hares push off with strong hind legs, leaving large hind prints ahead of smaller front prints. In deep snow, snowshoe hares make especially long tracks.
  
• Bounders: Weasels, martens, minks, and muskrats leap forward with both front and hind feet together, forming a repeating two-by-two pattern.
  
• Amblers: Raccoons, skunks, beavers, porcupines, and black bears move slowly, leaving wide tracks with visible claw marks.
  
• Perfect steppers: Foxes and coyotes place hind feet nearly where the front feet landed, creating straight lines. Coyotes tend to travel in direct paths, while domestic dogs wander in zigzags.

Challenges in interpreting animal tracks in snow

Despite various clues related to tracks, tracking animals in the snow can still be complex. Snow conditions affect how prints appear: fresh snow preserves details, while crusty, melting, or newly fallen snow can distort or cover tracks. 

Weather also plays a role, as wind and sunlight may blur edges, and overlapping paths from multiple animals can create confusion. Beyond this, animal behavior adds to the unpredictability. 

Some species, such as skunks and black bears, hibernate in winter, while others move erratically or retrace their trails. Meanwhile, in forests, tracks from deer, moose, or elk often intersect with smaller animals or predators, and additional signs like scat, fur, or tail drags are used to understand movement and behavior.

How computer vision can be used to detect animal tracks

To handle the challenges of analyzing animal tracks in snow, researchers are starting to turn to cutting-edge technology like computer vision. For instance, they are exploring computer vision models that can detect and localize individual prints, separate overlapping tracks, and even be trained on custom datasets to recognize species-specific features such as claw marks and gait patterns.

Specifically, models like Ultralytics YOLO11 support computer vision tasks like object detection, which can be used to identify and localize individual paw prints, and instance segmentation, which can separate overlapping tracks. By training a model like YOLO11 on a custom dataset of animal footprints, researchers can make it easier to recognize species-specific patterns, distinguish overlapping tracks, and generate more consistent results than manual observation.

Computer vision and research on animal footprint analysis

Now that we’ve seen how computer vision can track animal footprints, let’s walk through how this technology is being applied in real-world research.

From FIT to open datasets

For years, most footprint studies have relied on Footprint Identification Technology (FIT). FIT works by marking specific points on each footprint and using those measurements to tell animals apart. While effective, the process is slow, requires trained experts, and isn’t practical when you’re trying to analyze thousands of tracks in the wild.

A recent study on Amur tigers in Northeast China demonstrated how FIT can even identify individuals from footprints in snow, offering a reliable, non-invasive way to monitor endangered predators. 

However, the researchers also highlighted its limitations: it is labor-intensive and difficult to scale. Importantly, they noted that computer vision could automate this process in the future, reducing the need for manual measurements while handling much larger datasets.

That shift is already beginning with projects like OpenAnimalTracks, a public dataset containing thousands of labeled footprints from 18 species across mud, sand, and snow. With resources like this, Vision AI models can be trained to automatically detect and classify footprints, making wildlife monitoring faster and more accessible.

By building on the foundations of FIT and combining them with open datasets and computer vision, conservation research is moving toward scalable systems that can track species and protect ecosystems - without ever disturbing the animals themselves.

Key takeaways

Animal tracks in the snow show how animals move, behave, and use their habitats. Reading them by hand takes patience and experience, but computer vision makes the process more efficient. Using tools like YOLO11 alongside human knowledge makes wildlife monitoring more streamlined, helps conservation efforts, and provides useful data for protecting species.

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