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Where's The Bear?: Automating Wildlife Image Processing Using IoT and Edge Cloud Systems

Where's The Bear?: Automating Wildlife Image Processing Using IoT and Edge Cloud Systems 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation Where's The Bear?- Automating Wildlife Image Processing Using IoT and Edge Cloud Systems Andy Rosales Elias, Nevena Golubovic, Chandra Krintz, and Rich Wolski Computer Science Dept Univ. of California Santa Barbara, California ABSTRACT We investigate the design and implementation of W here's T he Bear (WTB), an end-to-end, distributed, IoT system for wildlife monitoring. WTB implements a multi-tier (cloud, edge, sensing) system that integrates recent advances in machine learning based image processing to automatically classify animals in images from remote, motion-triggered camera traps. We use non-local, resourcerich, public/private cloud systems to train the machine learning models, and "in-the-field," resource-constrained edge systems to perform classification near the IoT sensing devices (cameras). We deploy WTB at the UCSB Sedgwick Reserve, a 6000 acre site for environmental research and use it to aggregate, manage, and analyze over 1.12M images. WTB integrates Google TensorFlow and OpenCV applications to perform automatic image classification and tagging. To avoid transferring large numbers of training images for TensorFlow over the low-bandwidth network linking Sedgwick to public clouds, we devise a technique that uses stock Google Images to construct a synthetic training set using only a small http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Where's The Bear?: Automating Wildlife Image Processing Using IoT and Edge Cloud Systems

Association for Computing Machinery — Apr 18, 2017

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References (67)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISBN
978-1-4503-4966-6
doi
10.1145/3054977.3054986
Publisher site
See Article on Publisher Site

Abstract

2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation Where's The Bear?- Automating Wildlife Image Processing Using IoT and Edge Cloud Systems Andy Rosales Elias, Nevena Golubovic, Chandra Krintz, and Rich Wolski Computer Science Dept Univ. of California Santa Barbara, California ABSTRACT We investigate the design and implementation of W here's T he Bear (WTB), an end-to-end, distributed, IoT system for wildlife monitoring. WTB implements a multi-tier (cloud, edge, sensing) system that integrates recent advances in machine learning based image processing to automatically classify animals in images from remote, motion-triggered camera traps. We use non-local, resourcerich, public/private cloud systems to train the machine learning models, and "in-the-field," resource-constrained edge systems to perform classification near the IoT sensing devices (cameras). We deploy WTB at the UCSB Sedgwick Reserve, a 6000 acre site for environmental research and use it to aggregate, manage, and analyze over 1.12M images. WTB integrates Google TensorFlow and OpenCV applications to perform automatic image classification and tagging. To avoid transferring large numbers of training images for TensorFlow over the low-bandwidth network linking Sedgwick to public clouds, we devise a technique that uses stock Google Images to construct a synthetic training set using only a small

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