Art gallery theorems and algorithms
Art gallery theorems and algorithms
Automatic Sensor Placement from Vision Task Requirements
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special Issue on Industrial Machine Vision and Computer Vision Technology:8MPart
Algorithms for automatic sensor placement to acquire complete and accurate information
Algorithms for automatic sensor placement to acquire complete and accurate information
Sensor planning for 3D object search
Computer Vision and Image Understanding
A Solution to the Next Best View Problem for Automated Surface Acquisition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraint-Based Sensor Planning for Scene Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Four-step Camera Calibration Procedure with Implicit Image Correction
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automatic Camera Placement for Image-Based Modeling
PG '99 Proceedings of the 7th Pacific Conference on Computer Graphics and Applications
Placing observers to cover a polyhedral terrain in polynomial time
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Real-Time Self-Calibrating Stereo Person Tracking Using 3-D Shape Estimation from Blob Features
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Optimal Camera Placement to Obtain Accurate 3D Point Positions
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Content and task-based view selection from multiple video streams
Multimedia Tools and Applications
Can you see me now? sensor positioning for automated and persistent surveillance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Modeling Coverage in Camera Networks: A Survey
International Journal of Computer Vision
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Vision based tracking systems for surveillance and motion capture rely on a set of cameras to sense the environment. The exact placement or configuration of these cameras can have a profound affect on the quality of tracking which is achievable. Although several factors contribute, occlusion due to moving objects within the scene itself is often the dominant source of tracking error. This work introduces a configuration quality metric based on the likelihood of dynamic occlusion. Since the exact geometry of occluders can not be known a priori, we use a probabilistic model of occlusion. This model is extensively evaluated experimentally using hundreds of different camera configurations and found to correlate very closely with the actual probability of feature occlusion.