Computing in Science and Engineering
KNIGHT/spl trade/: a real time surveillance system for multiple and non-overlapping cameras
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Markerless human articulated tracking using hierarchical particle swarm optimisation
Image and Vision Computing
Relative pose problem for non-overlapping surveillance cameras with known gravity vector
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Camera localization in distributed networks using trajectory estimation
Journal of Electrical and Computer Engineering
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In this article we present an automatic camera calibration algorithm using multiple trajectories in a multiple camera network with non-overlapping field-of-views (FOV). Visible trajectories within a camera FOV are assumed to be measured with respect to the camera local co-ordinate system. Calibration is performed by aligning each camera local co-ordinate system with a pre-defined global co-ordinate system using three steps. Firstly, extrinsic pair-wise calibration parameters are estimated using particle swarm optimisation and Kalman filtering. The resulting pair-wise calibration estimates are used to generate an initial estimate of network calibration parameters, which are corrected to account for accumulation errors using particle swarm optimisation-based local search. Finally, a Bayesian framework with Metropolis algorithm is adopted and the posterior distribution over the network calibration parameters are estimated. We validate our algorithm using studio and synthetic datasets and compare our approach with existing state-of-the-art algorithms.