ABSTRACT
Multiple object tracking within a network of cameras with overlapping fields of views has gained interest. The acquisition of images in an asynchronous manner hinders the practical implementation of such systems. Most of the previous work reported tests over short intervals, leaving the performance degradation due to asynchronous image acquisition unknown. In this work, we propose an online method to recover the synchronization error while tracking objects. The recovered error is fed back to trackers so as to restore their performance. The time synchronization error is measured by the mismatch in the epipolar constraint between the two cameras. We show that successful recovery of the synchronization error is possible when its product with the object motion speeds are within some limits.
- M. S. Arulampalam, S. Maskell, N. Gordonl, and T. Clapp. A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. on Signal Processing, 50(2):174--188, 2002. Google ScholarDigital Library
- Y. Caspi and M. Irani. Spatio-temporal alignment of sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(11):1409--1424, 2002. Google ScholarDigital Library
- Y. Caspi, D. Simakov, and M. Irani. Feature-based sequence-to-sequence matching. International Journal of Computer Vision, 68(1), 2006. Google ScholarDigital Library
- J. Ferryman. Pets 2009. http://www.cvg.rdg.ac.uk/PETS2009/, 2009.Google Scholar
- F. Fleuret, J. Berclaz, R. Lengagne, and P. Fua. Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. PAMI, 30(2):267--282, 2008. Google ScholarDigital Library
- L. Guan, J.-S. Franco, and M. Pollefeys. Multi-object shape estimation and tracking from silhouette cues. In IEEE Conf. CVPR, pages 1--8, 2008.Google ScholarCross Ref
- R. Hartley and A. Zisserman. Multi View Geometry in Computer Vision. Cambridge University Press, second edition edition, 2004. Google ScholarDigital Library
- M. Keck and J. W. Davis. Recovery and reasoning about occlusions in 3d using few cameras with applications to 3d tracking. IJCV, 95(3):240--264, 2011. Google ScholarDigital Library
- S. M. Khan and M. Shah. Tracking multiple occluding people by localizing on multiple scene planes. IEEE Trans. PAMI, 31(3):505--519, 2009. Google ScholarDigital Library
- D.-S. Lee. Effective gaussian mixture learning for video background subtraction. IEEE Trans. PAMI, 27(5):827--832, 2005. Google ScholarDigital Library
- M. Noguchi and T. Kato. Geometric and timing calibration for unsynchronized cameras using trajectories of a moving marker. In IEEE Workshop on Applications of Computer Vision, 2007. Google ScholarDigital Library
- S. N. Sinha and M. Pollefeys. Synchronization and calibration of camera networks from silhouettes. In Proc. 17th Intl. Conf. Pattern Recognition, pages 116--119, 2004. Google ScholarDigital Library
- F. Sivrikaya and B. Yener. Time synchronization in sensor networks: A survey. IEEE Network, 18(4):45--50, 2004. Google ScholarDigital Library
- G. P. Stein. Tracking from multiple view points: Self-calibration of space and time. In Proceedings of DARPA IU Workshop, pages 1037--1042, 1998.Google Scholar
- O. Topcu. Icdsc2014 paper id 16 demo. http://youtu.be/YMycaf8xkjc, 2014.Google Scholar
- O. Topcu, A. A. Alatan, and A. O. Ercan. Occlusion-aware 3d multiple object tracker with two cameras for visual surveillance. In 11th IEEE Intl. Conf. Advanced Video and Signal-Based Surveillance, 2014.Google ScholarCross Ref
- R. Tsai. A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses. IEEE Journal of Robotics and Automation, 3(4):323--344, 1987.Google ScholarCross Ref
- G. Welch and G. Bishop. An introduction to the kalman filter, 1995.Google Scholar
- A. Whitehead, R. Laganiere, and P. Bose. Temporal synchronization of video sequences in theory and in practice. In Proceedings of the IEEE Workshop on Motion and Video Computing, 2005. Google ScholarDigital Library
Index Terms
- Recovery of Temporal Synchronization Error through Online 3D Tracking with Two Cameras
Recommendations
Self-calibration of omnidirectional multi-cameras including synchronization and rolling shutter
Deal with consumer 360 cameras and spherical cameras without a privileged direction.Initialize the time offsets and intrinsic parameters using monocular structure-from-motion.Start multi-camera structure-from-motion with central and global shutter ...
Object tracking in the presence of occlusions using multiple cameras: A sensor network approach
This article describes a sensor network approach to tracking a single object in the presence of static and moving occluders using a network of cameras. To conserve communication bandwidth and energy, we combine a task-driven approach with camera subset ...
Towards Automatic 3D Pose Tracking through Polygon Mesh Approximation
Advances in Artificial Intelligence – IBERAMIA 2012AbstractA method for visual 3D pose tracking of objects whose shape can be approximated to a polygon mesh it’s presented. The proposed method takes advantage of the fact that polygon meshes may be composed of quadrilaterals, which can be tracked in 2D ...
Comments