The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous Multi-Views Tracking using Tensor Voting
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
View-invariant Alignment and Matching of Video Sequences
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Synchronization of multi-camera video recordings based on audio
Proceedings of the 15th international conference on Multimedia
Synchronizing Video Sequences from Temporal Epipolar Lines Analysis
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Auto-organized visual perception using distributed camera network
Robotics and Autonomous Systems
Video synchronization and its application to object transfer
Image and Vision Computing
Temporal synchronization of non-overlapping videos using known object motion
Pattern Recognition Letters
Video synchronization using temporal signals from Epipolar lines
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Warping trajectories for video synchronization
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
Proceedings of the 10th European Conference on Visual Media Production
| Hi-index | 0.00 |
In this work, we present a formalization of the video synchronization problem that exposes new variants of the problem that have been left unexplored to date. We also present a novel method to temporally synchronize multiple stationary video cameras with overlapping views that: 1) does not rely on certain scene properties, 2) suffices for all variants of the synchronization problem exposed by the theoretical disseration, and 3) does not rely on the trajectory correspondence problem to be solved apriori. The method uses a two stage approach that first approximates the synchronization by tracking moving objects and identifying inflection points. The method then proceeds to refine the estimate using a consensus based matching heuristic to find moving features that best agree with the pre-computed camera geometries from stationary image features. By using the fundamental matrix and the trifocal tensor in the second refinement step we are able to improve the estimation of the first step and handle a broader range of input scenarios and camera conditions.