StartleCam: A Cybernetic Wearable Camera
ISWC '98 Proceedings of the 2nd IEEE International Symposium on Wearable Computers
ISWC '98 Proceedings of the 2nd IEEE International Symposium on Wearable Computers
Unsupervised clustering of ambulatory audio and video
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Context-based video retrieval system for the life-log applications
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Efficient retrieval of life log based on context and content
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Person tracking and multicamera video retrieval using floor sensors in a ubiquitous environment
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Novel concept for video retrieval in life log application
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Multimedia retrieval from a large number of sources in a ubiquitous environment
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
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Digitization of lengthy personal experiences would be made possible by constant recording using wearable video cameras. It is conceivable that the resulting amount of video content would be extraordinarily large. In order to retrieve and browse the desired scenes, a vast amount of video would need to be organized with structural information. In this paper, we attempt to develop a "wearable imaging system" that is capable of constantly capturing data, not only from a wearable video camera, but also from various kinds of sensors, such as a GPS, an accelerometer and a gyro sensor. The data from these sensors are appropriately extracted and processed by hidden Markov model (HMM) to achieve efficient video retrieval and browsing.