Krintz & Wolski "Where’s the Bear?"
System created by CE Profs. Krintz & Wolski bringing machine learning to the task of identifying and classifying animals caught on camera (COE Convergence)
CE Professors Chandra Krintz & Rich Wolski
System created by computer engineering professors bringing machine learning to the task of identifying and classifying animals caught on camera
Millions of images of animals — mountain lions, black bears, deer, and many other species of interest — have been captured by camera traps on the 6,000-acre Sedgwick Ranch Reserve, part of UC Santa Barbara’s Natural Reserve System. The images are a treasure trove of information that could be immensely useful to land managers and ecologists, but most remain stored on hard drives — unsorted, uncatalogued, inaccessible, and, thus, unused.
Now a system created by UCSB computer science faculty members Chandra Krintz and Rich Wolski, aptly named “Where’s the Bear?” is bringing machine learning to the task of identifying and classifying animals caught on camera.
Assigning to computers a vexing task that until now was the sole purview of people saves enormous manpower — what once took fourteen days to do can now be done in three hours — and the approach has potential far beyond Sedgwick to other reserves, and beyond ecology to agriculture and even medical imaging.
Where’s the Bear works well, notes Krintz, vice chair of UCSB’s undergraduate program in computer science. “We don’t get any coyotes wrong. We don’t get any bears wrong. We get about 12-percent error on deer — there are lots of deer — and we are trying to improve on that. Now, all the ecologists are saying, ‘Count deer, count bear. Tell me if the bear is healthy. Is it the same bear, is it the same deer? How many deer are there with antlers?’”
Where’s the Bear integrates recent advances in machine-learning-based image processing to automatically classify animals in images captured by remote, motion-triggered camera traps. So far, the system has helped the Sedgwick team aggregate and analyze more than 1 million images. And because the hardware lives at Sedgwick, all the data processing is done within yards of where the data is collected.
