Some NP-complete problems in quadratic and nonlinear programming
Mathematical Programming: Series A and B
Spatio-Temporal Alignment of Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Temporal Synchronization of Video Sequences in Theory and in Practice
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Feature-Based Sequence-to-Sequence Matching
International Journal of Computer Vision
Synchronization of Video Sequences from Free-Moving Cameras
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
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
Space-Time Body Pose Estimation in Uncontrolled Environments
3DIMPVT '11 Proceedings of the 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission
Simultaneously Estimating the Fundamental Matrix and Homographies
IEEE Transactions on Robotics
Event Dynamics Based Temporal Registration
IEEE Transactions on Multimedia
Tri-focal tensor-based multiple video synchronization with subframe optimization
IEEE Transactions on Image Processing
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Temporal synchronization of multiple video recordings of the same dynamic event is a critical task in many computer vision applications e.g. novel view synthesis and 3D reconstruction. Typically this information is implied, since recordings are made using the same timebase, or time-stamp information is embedded in the video streams. Recordings using consumer grade equipment do not contain this information; hence, there is a need to temporally synchronize signals using the visual information itself. Previous work in this area has either assumed good quality data with relatively simple dynamic content or the availability of precise camera geometry. In this paper, we propose a technique which exploits feature trajectories across views in a novel way, and specifically targets the kind of complex content found in consumer generated sports recordings, without assuming precise knowledge of fundamental matrices or homographies. Our method automatically selects the moving feature points in the two unsynchronized videos whose 2D trajectories can be best related, thereby helping to infer the synchronization index. We evaluate performance using a number of real recordings and show that synchronization can be achieved to within 1 sec, which is better than previous approaches.