Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed of\ufb02ine with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at a premium. In this paper, we present Vigil, a real-time distributed wireless surveillance system that leverages edge computing to support real-time tracking and surveillance in enterprise campuses, retail stores, and across smart cities. Vigil intelligently partitions video processing between edge computing nodes co-located with cameras and the cloud to save wireless capacity, which can then be dedicated to Wi-Fi hotspots, offsetting their cost. Novel video frame prioritization and traf\ufb01c scheduling algorithms further optimize Vigil\u2019s bandwidth utilization. We have deployed Vigil across three sites in both whitespace and Wi-Fi networks. Depending on the level of activity in the scene, experimental results show that Vigil allows a video surveillance system to support a geographical area of coverage between \ufb01ve and 200 times greater than an approach that simply streams video over the wireless network. For a \ufb01xed region of coverage and bandwidth, Vigil outperforms the default equal throughput allocation strategy of Wi-Fi by delivering up to 25% more objects relevant to a user\u2019s query.<\/p>\n
Internet-enabled cameras pervade daily life, generating a huge amount of data, but most of the video they generate is transmitted over wires and analyzed of\ufb02ine with a human in the loop. The ubiquity of cameras limits the amount of video that can be sent to the cloud, especially on wireless networks where capacity is at […]<\/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":[13555,13547],"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-422313","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"MobiCom '15","msr_affiliation":"","msr_published_date":"2015-09-07","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"978-1-4503-3619-2\/15\/09","msr_journal":"","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":"422316","msr_publicationurl":"","msr_doi":"10.1145\/2789168.2790123","msr_publication_uploader":[{"type":"file","title":"Bahl-MobiCom-2015","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/08\/Bahl-MobiCom-2015.pdf","id":422316,"label_id":0},{"type":"doi","title":"10.1145\/2789168.2790123","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Tan Zhang","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Aakanksha Chowdhery","user_id":30768,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Aakanksha Chowdhery"},{"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":"text","value":"Kyle Jamieson","user_id":0,"rest_url":false},{"type":"text","value":"Suman Banerjee","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[144899],"msr_project":[382664,212082],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","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. 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