GlimpseData: Towards Continuous Vision-Based Personal Analytics

Content Provider	Microsoft Research
Author	Han, Seungyeop Rajalakshmi Philipose, Matthai Krishnamurthy, Arvind Wetherall, David
Copyright Year	2014
Abstract	Emerging wearable devices provide a new opportunity for mobile context-aware applications to use continuous audio/video sensing data as primitive inputs. Due to the highdatarate and compute-intensive nature of the inputs, it is important to design frameworks and applications to be efficient. We present the GlimpseData framework to collect and analyze data for studying continuous high-datarate mobile perception. As a case study, we show that we can use lowpowered sensors as a filter to avoid sensing and processing video for face detection. Our relatively simple mechanism avoids processing roughly 60% of video frames while missing only 10% of frames with faces in them.
DOI	10.1145/2611264.2611269
Language	English
Publisher	ACM Association for Computing Machinery Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full cita-tion on the first page
Publisher Date	2014-06-01
Access Restriction	Open
Rights Holder	Microsoft Corporation
Subject Keyword	Mobile computing
Content Type	Text
Resource Type	Proceeding