Tracking and data association
Elements of information theory
Elements of information theory
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Camera Parameter Selection for State Estimation with Applications in Object Recognition
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Learning Temporal Context in Active Object Recognition Using Bayesian Analysis
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Transinformation for Active Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Optimal Camera Placement for Automated Surveillance Tasks
Journal of Intelligent and Robotic Systems
Improvement of vision guided robotic accuracy using Kalman filter
Computers and Industrial Engineering
Online control of active camera networks for computer vision tasks
ACM Transactions on Sensor Networks (TOSN)
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In this paper we present an information theoretic framework that provides an optimality criterion for the selection of the best sensor data regarding state estimation of dynamic system. One relevant application in practice is tracking a moving object in 3-D using multiple sensors. Our approach extends previous and similar work in the area of active object recognition, i.e. state estimation of static systems. We derive a theoretically well founded metric based on the conditional entropy that is also close to intuition: select those camera parameters that result in sensor data containing most information for the following state estimation. In the case of state estimation with a non-linear Kalman filter we show how that metric can be evaluated in closed form.The results of real-time experiments prove the benefits of our general approach in the case of active focal length adaption compared to fixed focal lengths. The main impact of the work consists in a uniform probabilistic description of sensor data selection, processing and fusion.