Assessing face and speech consistency for monologue detection in video
Proceedings of the tenth ACM international conference on Multimedia
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Graphical Model for Audiovisual Object Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pictorial Structures for Object Recognition
International Journal of Computer Vision
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Object Tracking Through Simultaneous Long Occlusions and Split-Merge Conditions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Phase diffusion for the synchronization of heterogenous sensor streams
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Efficient visual object tracking with online nearest neighbor classifier
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Speaker association with signal-level audiovisual fusion
IEEE Transactions on Multimedia
Audiovisual Synchronization and Fusion Using Canonical Correlation Analysis
IEEE Transactions on Multimedia
Linear time offline tracking and lower envelope algorithms
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Motion synchrony, i.e., the coordinated motion of a group of individuals, is an interesting phenomenon in nature or daily life. Fish swim in schools, birds fly in flocks, soldiers march in platoons, etc. Our goal is to detect motion synchrony that may be present in the video data, and to track the group of moving objects as a whole. This opens the door to novel algorithms and applications. To this end, we model individual motions as video tubes in space-time, define motion synchrony by the geometric relation among video tubes, and track a whole set of tubes by dynamic programming. The resulting algorithm is highly efficient in practice. Given a video clip of T frames of resolution XxY, we show that finding the K spatially correlated video tubes and determining the presence of synchrony can be solved optimally in O(XYTK) time. Preliminary experiments show that our method is both effective and efficient. Typical running times are 30 - 100 VGA-resolution frames per second after feature extraction, and the accuracy for the detection of synchrony is more than 90% as evaluated in our annotated data set.