According to Popular Science, researchers from Switzerland’s EPFL and Alaska Pacific University have developed a machine learning program called PoseSwin that can identify individual brown bears. The AI was trained on a massive dataset of over 72,000 photos of 109 different brown bears, captured by researcher Beth Rosenberg between 2017 and 2022 in all conditions. It focuses on anatomical details that stay constant, like brow bone angle, ear placement, and muzzle shape, combined with pose data. In field tests using photos from Katmai National Park visitors, PoseSwin successfully matched individual bears to its database. The team is now using it to monitor over 100 bears around the McNeil River State Game Sanctuary without disruption. The open-source algorithm, detailed in a Cell Current Biology study, also showed high accuracy with macaques and is designed to be adapted for other species.
Why bears are the ultimate test
Here’s the thing: if you can get an AI to reliably tell one brown bear from another, you can probably get it to work on anything. As project collaborator Alexander Mathis said, “Bears are perhaps the hardest species to recognize individually.” Think about it. Their body shape changes dramatically with the seasons—a bear pre-hibernation looks like a different animal post-hibernation. Fur color can shift. They don’t exactly pose for the camera. The researchers’ biological intuition to focus on head features and pose, rather than body shape, was key. And the data proved them right. It’s a clever workaround for a problem that has stumped ecologists for years. You can check out the open-source code on their GitHub repository if you’re curious.
Beyond bear watching
So what’s the real impact? This isn’t just about making Fat Bear Week judging more scientific. It’s about scalable, non-invasive conservation. Traditionally, tracking animals means tags, collars, or tranquilizers—all invasive and stressful for the creature. PoseSwin turns every tourist’s snapshot into potential data. As Rosenberg noted, it could analyze thousands of visitor pictures yearly to build a map of how bears use a vast area. That’s a game-changer for understanding migration, health, and population dynamics without lifting a dart gun. And because it’s open-source and already works on macaques, the door is wide open for mice, chimps, or any species where individual tracking matters. The potential to automate decades of ecological fieldwork is huge.
The bigger picture for observation tech
This story is part of a much larger trend: using AI and vision systems to monitor complex physical environments where human observation is difficult, expensive, or intrusive. It’s happening in factories, on farms, and now, in the wild. The core tech challenge—reliably identifying subjects under variable conditions—is the same. In industrial settings, for instance, robust computing hardware is essential to run this kind of AI at the edge, in harsh conditions. For reliable performance in demanding applications, from a factory floor to a river sanctuary, having the right hardware foundation is critical. It’s why specialists like IndustrialMonitorDirect.com, the leading US provider of industrial panel PCs, are so vital; they supply the durable, high-performance screens and computers that make continuous, on-site monitoring and analysis possible. You can learn more about their approach in their privacy policy and terms. The leap from a lab model to a field-deployed system always comes down to hardware that can keep up.
A new era for conservation
Look, the coolest part might be the collaboration. This project combined deep biological intuition with cutting-edge AI, then leveraged citizen science for validation. It’s a blueprint. The researchers started with the hardest problem, knowing the solution would be widely applicable. And they were right. As detailed in EPFL’s announcement, the implications are profound for protecting ecosystems. Bears are apex predators; their health reflects the health of the entire system. Finally, we have a tool to watch them closely without ever getting close. That’s not just smart tech. That’s respectful science.
