Senior Computer Vision Research Engineer, Wild Me
Computer Science Ph.D. Candidate, Rensselaer Polytechnic Institute, Troy, NY
As the price of photography and video equipment drops while availability improves, visual data from the public is becoming the most abundant source of wildlife data. However, curating this high volume of data from "citizen science" and social media poses new scalability challenges for both researchers and computer scientists alike. This talk introduces the Wildbook platform -- an open-source web-based project (wildbook.org) -- that leverages a suite of deep learning tools to automatically process high volumes of image data for conservation. The platform uses Convolutional Neural Networks (CNNs) on several computer vision tasks, including: image classification, bounding box regression, instance classification, class segmentation, and object of interest classification. Our deep learning stack utilizes Theano and PyTorch on the NVIDIA's CUDA, CNMeM, and CuDNN deep learning stack and employs multiple Titan V GPUs for efficient processing. Further, instance recognition allows Wildbook to individually identify individual animals through time using a pipeline of SIFT features to find distinctive patches, Approximate Nearest Neighbors to find visual neighbors, and LNBNN to associate and re-rank visual match correspondences. A random forest pair-wise match verifier is then used to curate animal sightings for human-in-the-loop population curation. Lastly, our computer vision pipeline works alongside an intelligent agent that can automatically ingest video data from YouTube using NLP and OCR with Azure Cognitive Services. We present our conservation work for wildlife across the globe in the context of the latest advances in deep learning and how Wildbook is helping to convert wildlife conservation into a data-driven science.
Jason Parham received his B.S. in Computer Science / Mathematics from Pepperdine University in Malibu, CA in 2008 and holds a M.S. in Computer Science from RPI in Troy, NY. Jason is finishing his Ph.D. as a candidate under the advising of Dr. Charles Stewart at RPI. Jason’s Masters thesis was on the design and implementation of a citizen science-powered photographic censusing of the zebra and giraffe in the Nairobi National Park. His current doctoral research focuses on animal detection and classification, using deep learning on wildlife imagery, to power automated photographic censusing. Jason is a co-developer of Wildbook‘s Image Analysis components, which are used to monitor animal populations in conservancies around Kenya and which integrate with the Wildbook data management platform for a suite of species. Previously, Jason worked three years for Kitware, Inc. in Clifton Park on detecting vehicles and military aircraft in overhead satellite imagery. Below are some select publications:
Monday, February 25, 2019 at 4:00pm to 4:50pm
Weniger Hall, 151
103 SW Memorial Place, Corvallis, OR 97331