TY - GEN
T1 - Communication-efficient multi-view keyframe extraction in distributed video sensors
AU - Ou, Shun Hsing
AU - Lu, Yu Chen
AU - Wang, Jui Pin
AU - Chien, Shao Yi
AU - Lin, Shou De
AU - Yeti, Mi Yen
AU - Lee, Chia Han
AU - Gibbons, Phillip B.
AU - Somayazulu, V. Srinivasa
AU - Chen, Yen Kuang
PY - 2015/2/27
Y1 - 2015/2/27
N2 - Video sensors are widely used in many applications such as security monitoring and home care. However, the growth of the number of sensors makes it impractical to stream all videos back to a central server for further processing, due to communication bandwidth and server storage constraints. Multi-view video summarization allows us to discard redundant data in the video streams taken by a group of sensors. All prior multi-view summarization methods, however, process video data in an off-line and centralized manner, which means that all videos are still required to be streamed back to the server before conducting the summarization. This paper proposes an on-line, distributed multi-view summarization system, which integrates the ideas of Maximal Marginal Relevance (MMR) and MS-Wave, a bandwidth-efficient distributed algorithm for finding k-nearest-neighbors and k-farthest-neighbors. Empirical studies show that our proposed system can discard redundant videos and keep important keyframes as effectively as centralized approaches, while transmitting only 1/6 to 1/3 as much data.
AB - Video sensors are widely used in many applications such as security monitoring and home care. However, the growth of the number of sensors makes it impractical to stream all videos back to a central server for further processing, due to communication bandwidth and server storage constraints. Multi-view video summarization allows us to discard redundant data in the video streams taken by a group of sensors. All prior multi-view summarization methods, however, process video data in an off-line and centralized manner, which means that all videos are still required to be streamed back to the server before conducting the summarization. This paper proposes an on-line, distributed multi-view summarization system, which integrates the ideas of Maximal Marginal Relevance (MMR) and MS-Wave, a bandwidth-efficient distributed algorithm for finding k-nearest-neighbors and k-farthest-neighbors. Empirical studies show that our proposed system can discard redundant videos and keep important keyframes as effectively as centralized approaches, while transmitting only 1/6 to 1/3 as much data.
UR - http://www.scopus.com/inward/record.url?scp=84925398089&partnerID=8YFLogxK
U2 - 10.1109/VCIP.2014.7051492
DO - 10.1109/VCIP.2014.7051492
M3 - Conference contribution
AN - SCOPUS:84925398089
T3 - 2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014
SP - 13
EP - 16
BT - 2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Visual Communications and Image Processing Conference, VCIP 2014
Y2 - 7 December 2014 through 10 December 2014
ER -