@inproceedings{han2014glimpsedata,
author = {Han, Seungyeop and Nandakumar, Rajalakshmi and Philipose, Matthai and Krishnamurthy, Arvind and Wetherall, David},
title = {GlimpseData: Towards Continuous Vision-Based Personal Analytics},
booktitle = {Workshop on Physical Analytics},
year = {2014},
month = {June},
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.},
publisher = {ACM - Association for Computing Machinery},
url = {http://approjects.co.za/?big=en-us/research/publication/glimpsedata-towards-continuous-vision-based-personal-analytics/},
edition = {Workshop on Physical Analytics},
}