{"id":167037,"date":"2014-06-01T00:00:00","date_gmt":"2014-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/glimpsedata-towards-continuous-vision-based-personal-analytics\/"},"modified":"2018-10-16T21:31:18","modified_gmt":"2018-10-17T04:31:18","slug":"glimpsedata-towards-continuous-vision-based-personal-analytics","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/glimpsedata-towards-continuous-vision-based-personal-analytics\/","title":{"rendered":"GlimpseData: Towards Continuous Vision-Based Personal Analytics"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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 <em>GlimpseData<\/em> 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 <em>filter<\/em> 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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-167037","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"ACM - Association for Computing Machinery","msr_edition":"Workshop on Physical 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Han","user_id":0,"rest_url":false},{"type":"text","value":"Rajalakshmi Nandakumar","user_id":0,"rest_url":false},{"type":"user_nicename","value":"matthaip","user_id":32834,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=matthaip"},{"type":"text","value":"Arvind Krishnamurthy","user_id":0,"rest_url":false},{"type":"text","value":"David 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