Theory of linear and integer programming
Theory of linear and integer programming
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncertainty-Based Information: Elements of Generalized Information Theory
Uncertainty-Based Information: Elements of Generalized Information Theory
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Automatic Camera Selection and Fusion for Outdoor Surveillance under Changing Weather Conditions
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
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
The sensor selection problem for bounded uncertainty sensing models
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Multicamera People Tracking with a Probabilistic Occupancy Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time cooperative multi-target tracking by dense communication among Active Vision Agents
Web Intelligence and Agent Systems
Multi-camera people tracking using evidential filters
International Journal of Approximate Reasoning
A Robust and Efficient Approach for Human Tracking in Multi-camera Systems
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Maximum mutual information principle for dynamic sensor query problems
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
A multi-resolution particle filter tracking in a multi-camera environment
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Multimedia
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Tracking persons with multiple cameras with overlapping fields of view instead of with one camera leads to more robust decisions. However, operating multiple cameras instead of one requires more processing power and communication bandwidth, which are limited resources in practical networks. When the fields of view of different cameras overlap, not all cameras are equally needed for localizing a tracking target. When only a selected set of cameras do processing and transmit data to track the target, a substantial saving of resources is achieved. The recent introduction of smart cameras with on-board image processing and communication hardware makes such a distributed implementation of tracking feasible. We present a novel framework for selecting cameras to track people in a distributed smart camera network that is based on generalized information-theory. By quantifying the contribution of one or more cameras to the tracking task, the limited network resources can be allocated appropriately, such that the best possible tracking performance is achieved. With the proposed method, we dynamically assign a subset of all available cameras to each target and track it in difficult circumstances of occlusions and limited fields of view with the same accuracy as when using all cameras.