Art gallery theorems and algorithms
Art gallery theorems and algorithms
Simulated Annealing: A Proof of Convergence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Camera Placement for Image-Based Modeling
PG '99 Proceedings of the 7th Pacific Conference on Computer Graphics and Applications
Optimal Camera Placement to Obtain Accurate 3D Point Positions
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Dense Multiple View Stereo with General Camera Placement using Tensor Voting
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements
Computer Vision and Image Understanding - Special issue on omnidirectional vision and camera networks
A design methodology for selection and placement of sensors in multimedia surveillance systems
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Optimal Camera Placement for Automated Surveillance Tasks
Journal of Intelligent and Robotic Systems
A General Method for Sensor Planning in Multi-Sensor Systems: Extension to Random Occlusion
International Journal of Computer Vision
Can you see me now? sensor positioning for automated and persistent surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reversible jump MCMC simulated annealing for neural networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Hi-index | 0.00 |
The selection of optimal camera configurations (camera locations, orientations etc.) for multi-camera networks remains an unsolved problem. Previous approaches largely focus on proposing various objective functions to achieve different tasks. Most of them, however, do not generalize well to large scale networks. To tackle this, we introduce a statistical formulation of the optimal selection of camera configurations as well as propose a Trans-Dimensional Simulated Annealing (TDSA) algorithm to effectively solve the problem. We compare our approach with a state-of-the-art method based on Binary Integer Programming (BIP) and show that our approach offers similar performance on small scale problems. However, we also demonstrate the capability of our approach in dealing with large scale problems and show that our approach produces better results than 2 alternative heuristics designed to deal with the scalability issue of BIP.