{"id":391661,"date":"2017-06-19T13:31:01","date_gmt":"2017-06-19T20:31:01","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=391661"},"modified":"2022-04-11T10:29:08","modified_gmt":"2022-04-11T17:29:08","slug":"real-time-video-analytics-killer-app-edge-computing","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/real-time-video-analytics-killer-app-edge-computing\/","title":{"rendered":"Real-time Video Analytics: the killer app for edge computing"},"content":{"rendered":"

According to a 2015 report by IHS on the installed base for video surveillance equipment, there is a camera installed for every 29 people on the planet, with mature markets like the US having a camera for every eight of its citizens. The report expects the number of cameras to grow by 20% year-over-year<\/em> for the next five years. Video analytics from these cameras are used for traffic control, retail store monitoring, surveillance and security, as well as consumer applications including digital assistants for real-time<\/em> decisions.<\/p>\n

Our position is that a geo-distributed architecture of public clouds, private clusters and edges extending all the way down to compute at the cameras<\/em> is the only viable approach that can meet the strict requirements of real-time, large-scale video analytics.<\/p>\n

Besides low latency and efficient bandwidth usage, another major consideration for continuous video analytics is the high<\/em> compute cost of video processing<\/em>. Because of the high data volumes, compute demands, and latency requirements, we believe that cameras represent the most challenging of \u201cthings\u201d in Internet-of-Things, and large-scale video analytics may well represent the killer application<\/em> for edge computing.<\/p>\n","protected":false},"excerpt":{"rendered":"

According to a 2015 report by IHS on the installed base for video surveillance equipment, there is a camera installed for every 29 people on the planet, with mature markets like the US having a camera for every eight of its citizens. The report expects the number of cameras to grow by 20% year-over-year for […]<\/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":[13547],"msr-publication-type":[193715],"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-391661","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2017-10-3","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"IEEE Computer","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"391667","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/ieeexplore.ieee.org\/document\/8057318","label_id":"243109","label":0},{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/06\/CO_COMSI-2017-03-0045.R1_Ananthanarayanan.pdf","id":"391667","title":"CO_COMSI-2017-03-0045.R1_Ananthanarayanan","label_id":"243132","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Ganesh Ananthanarayanan","user_id":31834,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Ganesh Ananthanarayanan"},{"type":"user_nicename","value":"Victor Bahl","user_id":31167,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Victor Bahl"},{"type":"user_nicename","value":"Peter Bod\u00edk","user_id":33239,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Peter Bod\u00edk"},{"type":"user_nicename","value":"Krishna Chintalapudi","user_id":32577,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Krishna Chintalapudi"},{"type":"user_nicename","value":"Matthai Philipose","user_id":32834,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Matthai Philipose"},{"type":"user_nicename","value":"Lenin Ravindranath Sivalingam","user_id":32645,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Lenin Ravindranath Sivalingam"},{"type":"user_nicename","value":"Sudipta Sinha","user_id":33748,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sudipta Sinha"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144899],"msr_project":[382664,212082],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":382664,"post_title":"Microsoft Rocket for Live Video Analytics","post_name":"live-video-analytics","post_type":"msr-project","post_date":"2017-05-15 08:28:48","post_modified":"2020-11-22 08:59:49","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/live-video-analytics\/","post_excerpt":"Project Rocket's goal is to democratize video analytics: build a system for real-time, low-cost, accurate analysis of live videos. This system will work across a geo-distributed hierarchy of intelligent edges and large clouds, with the ultimate goal of making it easy and affordable for anyone with a camera stream to benefit from video analytics.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/382664"}]}},{"ID":212082,"post_title":"Edge Computing","post_name":"edge-computing","post_type":"msr-project","post_date":"2020-02-23 16:44:03","post_modified":"2020-11-12 19:40:46","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/edge-computing\/","post_excerpt":"Industries ranging from manufacturing to healthcare are eager to develop real-time control systems that use machine learning and artificial intelligence to improve efficiencies and reduce cost. 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