Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ACM SIGGRAPH 2004 Papers
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Alignment of Continuous Video onto 3D Point Clouds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Proceedings of the 4th workshop on Embedded networked sensors
Fast Generation of Dynamic and Multi-Resolution 360-Degree Panorama from Video Sequences
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Landmark-based pedestrian navigation from collections of geotagged photos
Proceedings of the 7th International Conference on Mobile and Ubiquitous Multimedia
CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
MoVi: mobile phone based video highlights via collaborative sensing
Proceedings of the 8th international conference on Mobile systems, applications, and services
PhotoCity: training experts at large-scale image acquisition through a competitive game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TagSense: a smartphone-based approach to automatic image tagging
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Automatic tag generation and ranking for sensor-rich outdoor videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Discrete-continuous optimization for large-scale structure from motion
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
CoMon: cooperative ambience monitoring platform with continuity and benefit awareness
Proceedings of the 10th international conference on Mobile systems, applications, and services
Satellites in our pockets: an object positioning system using smartphones
Proceedings of the 10th international conference on Mobile systems, applications, and services
Spatiotemporal sequence matching for efficient video copy detection
IEEE Transactions on Circuits and Systems for Video Technology
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
International Journal of Mobile Human Computer Interaction
FOCUS: clustering crowdsourced videos by line-of-sight
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
Understand Instant Video Clip Sharing on Mobile Platforms: Twitter's Vine as a Case Study
Proceedings of Network and Operating System Support on Digital Audio and Video Workshop
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Crowdsourced video often provides engaging and diverse perspectives not captured by professional videographers. Broad appeal of user-uploaded video has been widely confirmed: freely distributed on YouTube, by subscription on Vimeo, and to peers on Facebook/Google+. Unfortunately, user-generated multimedia can be difficult to organize; these services depend on manual "tagging" or machine-mineable viewer comments. While manual indexing can be effective for popular, well-established videos, newer content may be poorly searchable; live video need not apply. We envisage video-sharing services for live user video streams, indexed automatically and in realtime, especially by shared content. We propose FOCUS, for Hadoop-on-cloud video-analytics. FOCUS uniquely leverages visual, 3D model reconstruction and multimodal sensing to decipher and continuously track a video's line-of-sight. Through spatial reasoning on the relative geometry of multiple video streams, FOCUS recognizes shared content even when viewed from diverse angles and distances. In a 70-volunteer user study, FOCUS' clustering correctness is roughly comparable to humans.