{"id":186089,"date":"2011-03-21T00:00:00","date_gmt":"2011-03-28T19:13:03","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/object-centered-visual-recognition\/"},"modified":"2016-08-22T11:30:26","modified_gmt":"2016-08-22T18:30:26","slug":"object-centered-visual-recognition","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/object-centered-visual-recognition\/","title":{"rendered":"Object-Centered Visual Recognition"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Machine vision approaches for accurate and efficient visual recognition have the potential to play a transformative role in numerous applications, and while seemingly simple visual tasks continue to elude artificial systems, we are making rapid progress. In this talk I will discuss my research on \u201cobject-centered\u201d visual recognition, where the goal is to find objects in images and videos and characterize their attributes and activity in detail. Specifically, I will discuss my work on four fundamental aspects of object-centered visual recognition: object detection, pose estimation, object tracking and behavior recognition. First, I will describe our state-of-the-art pedestrian detection approach with a focus on a recent insight that has allowed us to perform accurate multiscale detection in near real time. This approach yields a speedup of 10-100 times over competing methods with only a minor loss in detection accuracy and the underlying theory should be readily applicable to numerous domains. I will also briefly discuss our large-scale benchmarking of pedestrian detection, highlighting current successes and open challenges for the research community. In the second part of the talk I will describe our method for efficient pose estimation, a general tracking by detection system which leverages our research in object detection and pose estimation, and our widely adopted framework for behavior recognition. The approaches I will present are both effective (i.e., accurate and robust) and practical (i.e., computationally efficient and broadly applicable) and these are elements I will highlight throughout the talk.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine vision approaches for accurate and efficient visual recognition have the potential to play a transformative role in numerous applications, and while seemingly simple visual tasks continue to elude artificial systems, we are making rapid progress. In this talk I will discuss my research on \u201cobject-centered\u201d visual recognition, where the goal is to find objects [&hellip;]<\/p>\n","protected":false},"featured_media":196065,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-186089","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/4YZ0gKxxWkA","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/186089","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/186089\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/196065"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=186089"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=186089"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=186089"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=186089"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=186089"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=186089"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=186089"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=186089"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=186089"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=186089"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}