Kalman filtering: theory and practice
Kalman filtering: theory and practice
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Optimal Camera Parameter Selection in Kalman Filter Based Object Tracking
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Optimal Camera Parameter Selection for State Estimation with Applications in Object Recognition
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Design of many-camera tracking systems for scalability and efficient resource allocation
Design of many-camera tracking systems for scalability and efficient resource allocation
Mixed scale motion recovery
Information Theoretic Focal Length Selection for Real-Time Active 3-D Object Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Scheduling an active camera to observe people
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Acquisition of high-resolution images through on-line saccade sequence planning
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Surveillance camera scheduling: a virtual vision approach
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Constructing task visibility intervals for a surveillance system
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
A general method for comparing the expected performance of tracking and motion capture systems
Proceedings of the ACM symposium on Virtual reality software and technology
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
Towards on-line saccade planning for high-resolution image sensing
Pattern Recognition Letters - Special issue on vision for crime detection and prevention
Towards intelligent camera networks: a virtual vision approach
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A General Method for Sensor Planning in Multi-Sensor Systems: Extension to Random Occlusion
International Journal of Computer Vision
Hardware design optimization for human motion tracking systems
Hardware design optimization for human motion tracking systems
Task scheduling in large camera networks
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Multi-view dynamic scene modeling
Multi-view dynamic scene modeling
On-line control of active camera networks
On-line control of active camera networks
Multi-step multi-camera view planning for real-time visual object tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Active-vision-based multisensor surveillance - an implementation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An information theoretic criterion for evaluating the quality of 3-D reconstructions from video
IEEE Transactions on Image Processing
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Large networks of cameras have been increasingly employed to capture dynamic events for tasks such as surveillance and training. When using active cameras to capture events distributed throughout a large area, human control becomes impractical and unreliable. This has led to the development of automated approaches for online camera control. We introduce a new automated camera control approach that consists of a stochastic performance metric and a constrained optimization method. The metric quantifies the uncertainty in the state of multiple points on each target. It uses state-space methods with stochastic models of target dynamics and camera measurements. It can account for occlusions, accommodate requirements specific to the algorithms used to process the images, and incorporate other factors that can affect their results. The optimization explores the space of camera configurations over time under constraints associated with the cameras, the predicted target trajectories, and the image processing algorithms. The approach can be applied to conventional surveillance tasks (e.g., tracking or face recognition), as well as tasks employing more complex computer vision methods (e.g., markerless motion capture or 3D reconstruction).