An integrated scheme for object-based video abstraction
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Where Are the Ball and Players? Soccer Game Analysis with Color Based Tracking and Image Mosaick
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ACM SIGGRAPH 2003 Papers
Automatic Video Summarization by Graph Modeling
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Across Multiple Cameras With Disjoint Views
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Exact and Approximate Graph Matching Using Random Walks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scenario based dynamic video abstractions using graph matching
Proceedings of the 13th annual ACM international conference on Multimedia
Making a Long Video Short: Dynamic Video Synopsis
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Trajectory Association across Multiple Airborne Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonchronological Video Synopsis and Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A set theoretical method for video synopsis
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
COMPSAC '09 Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 02
Trajectory Association and Fusion across Partially Overlapping Cameras
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Multi-feature graph-based object tracking
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Inter-camera association of multi-target tracks by on-line learned appearance affinity models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Reweighted random walks for graph matching
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Key object-based static video summarization
MM '11 Proceedings of the 19th ACM international conference on Multimedia
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Special issue on Multimedia Event Detection
Machine Vision and Applications
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Nowadays, tremendous amount of video is captured endlessly from increased numbers of video cameras distributed around the world. Since needless information is abundant in the raw videos, making video browsing and retrieval is inefficient and time consuming. Video synopsis is an effective way to browse and index such video, by producing a short video representation, while keeping the essential activities of the original video. However, video synopsis for single camera is limited in its view scope, while understanding and monitoring overall activity for large scenarios is valuable and demanding. To solve the above issues, we propose a novel video synopsis algorithm for partially overlapping camera network. Our main contributions reside in three aspects: First, our algorithm can generate video synopsis for large scenarios, which can facilitate understanding overall activities. Second, for generating overall activity, we adopt a novel unsupervised graph matching algorithm to associate trajectories across cameras. Third, a novel multiple kernel similarity is adopted in selecting key observations for eliminating content redundancy in video synopsis. We have demonstrated the effectiveness of our approach on real surveillance videos captured by our camera network.