{"id":551334,"date":"2018-11-16T11:31:22","date_gmt":"2018-11-16T19:31:22","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=551334"},"modified":"2019-04-29T15:24:13","modified_gmt":"2019-04-29T22:24:13","slug":"farsight-a-smartphone-based-vehicle-ranging-system","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/farsight-a-smartphone-based-vehicle-ranging-system\/","title":{"rendered":"FarSight: A Smartphone-based Vehicle Ranging System"},"content":{"rendered":"

Maintaining an adequate separation from the vehicle in front is key to safe driving. While LIDAR and RADAR sensors\u00a0could be used for ranging, cost considerations and the huge installed base of vehicles that lack these sensors, especially in\u00a0developing regions, call for a low-cost yet robust alternative. To this end, we present\u00a0<\/span>FarSight<\/span>, a system that performs vehicle\u00a0ranging using a smartphone mounted on the windshield or the dashboard.<\/span>FarSight <\/span>uses the smartphone\u2019s camera to identify\u00a0and draw a bounding box around vehicles in front, based on which ranging is performed. Unlike prior smartphone-based\u00a0work,\u00a0<\/span>FarSight <\/span>does not depend on any infrastructure support such as standard-width lane markers and works with a\u00a0heterogeneous mix of vehicles, both of which are characteristics of developing regions. We develop a novel hybrid approach\u00a0for vehicle detection and tracking, which balances accuracy and speed by combining deep neural network based vehicle\u00a0detection with vision-based object tracking in a pipelined manner. We also devise data augmentation techniques to improve\u00a0the e\ufb00ectiveness of vehicle detection, thereby increasing the ranging distance.<\/span><\/p>\n


\nWe show that<\/span>FarSight <\/span>can range accurately in both daytime and nighttime conditions and up to distances of 90 m. We\u00a0have implemented<\/span>FarSight <\/span>as an Android-app and tested it across various phones. Further, we present two ranging-based\u00a0applications built on\u00a0<\/span>FarSight.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Maintaining an adequate separation from the vehicle in front is key to safe driving. While LIDAR and RADAR sensors\u00a0could be used for ranging, cost considerations and the huge installed base of vehicles that lack these sensors, especially in\u00a0developing regions, call for a low-cost yet robust alternative. To this end, we present\u00a0FarSight, a system that performs […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562,13547],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-551334","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2018-12-15","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"ACM IMWUT\/UBICOMP","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":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/dl.acm.org\/citation.cfm?id=3287059","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Akshay Uttama Nambi S. N","user_id":38169,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Akshay Uttama Nambi S. N"},{"type":"text","value":"Aditya Virmani","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Venkat Padmanabhan","user_id":33180,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Venkat Padmanabhan"}],"msr_impact_theme":[],"msr_research_lab":[199562],"msr_event":[],"msr_group":[602169],"msr_project":[320399],"publication":[],"video":[],"download":[],"msr_publication_type":"article","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/551334"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/551334\/revisions"}],"predecessor-version":[{"id":551352,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/551334\/revisions\/551352"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=551334"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=551334"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=551334"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=551334"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=551334"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=551334"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=551334"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=551334"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=551334"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=551334"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=551334"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=551334"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=551334"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=551334"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=551334"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}