Tracking and data association
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Zoom tracking and its applications
Machine Vision and Applications
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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
Online control of active camera networks for computer vision tasks
ACM Transactions on Sensor Networks (TOSN)
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We present a new method for planning the optimal next view for a probabilistic visual object tracking task. Our method uses a variable number of cameras, can plan an action sequence several time steps into the future, and allows for real-time usage due to a computation time which is linear both in the number of cameras and the number of time steps. The algorithm can also handle object loss in one, more or all cameras, interdependencies in the camera's information contribution, and variable action costs. We evaluate our method by comparing it to previous approaches with a prerecorded sequence of real world images.