Viewable scene modeling for geospatial video search
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Energy-accuracy trade-off for continuous mobile device location
Proceedings of the 8th international conference on Mobile systems, applications, and services
W2Go: a travel guidance system by automatic landmark ranking
Proceedings of the international conference on Multimedia
Web-Scale Multimedia Analysis: Does Content Matter?
IEEE MultiMedia
Keyframe presentation for browsing of user-generated videos on map interfaces
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Automatic tag generation and ranking for sensor-rich outdoor videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Multi-video summary and skim generation of sensor-rich videos in geo-space
Proceedings of the 3rd Multimedia Systems Conference
MediaQ: mobile multimedia management system
Proceedings of the 5th ACM Multimedia Systems Conference
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Video associated positioning data has become a useful contextual feature to facilitate analysis and management of media assets in GIS and social media applications. Moreover, with today's sensor-equipped mobile devices, the location of a camera can be continuously acquired in conjunction with the captured video stream without much difficulty. However, most sensor information collected from mobile devices is not highly accurate due to two main reasons: (a) the varying surrounding environmental conditions during data acquisition, and (b) the use of low-cost, consumer-grade sensors in current mobile devices. In this paper, we enhance the noisy positioning data generated by smartphones during video recording by analyzing typical error patterns for real collected data and introducing two robust algorithms, based on Kalman filtering and weighted linear least square regression, respectively. Our experimental results demonstrate significant benefits of our methods, which help upstream sensor-aided applications to access media content precisely.