Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/VCIP.2014.7051492 | VCIP |
Keywords | Field | DocType |
video streams,video signal processing,distributed algorithms,video data process,server storage constraint,k-nearest-neighbors,communication bandwidth constraint,k-farthest-neighbors,home care,distributed video sensors,bandwidth-efficient distributed algorithm,image sensors,feature extraction,multiview video summarization,security monitoring,maximal marginal relevance,distributed multiview summarization system,video streaming,communication-efficient multiview keyframe extraction,ms-wave | Automatic summarization,Video processing,Video sensors,Computer science,Security monitoring,Multiview Video Coding,Real-time computing,Video tracking,Distributed algorithm,Empirical research | Conference |
Citations | PageRank | References |
3 | 0.37 | 10 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shun-Hsing Ou | 1 | 34 | 2.38 |
| Yu-Chen Lu | 2 | 20 | 2.16 |
| Jui-Pin Wang | 3 | 31 | 5.57 |
| Shao-Yi Chien | 4 | 1603 | 154.48 |
| Shou-De Lin | 5 | 706 | 84.81 |
| Mi-Yen Yeti | 6 | 3 | 0.37 |
| Chia-han Lee | 7 | 607 | 55.50 |
| Phillip B. Gibbons | 8 | 6863 | 624.14 |
| V. Srinivasa Somayazulu | 9 | 112 | 10.47 |
| Yen-Kuang Chen | 10 | 888 | 95.79 |