Stochastic simulation
Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
System architecture directions for networked sensors
ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Sensor selection: a geometrical approach
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 2 - Volume 2
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
A method of reactive zoom control from uncertainty in tracking
Computer Vision and Image Understanding
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Learning sensor-based navigation of a real mobile robot in unknownworlds
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Vision sensor planning for 3-D model acquisition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptation and Change Detection With a Sequential Monte Carlo Scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimized particles for 3-D tracking
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Robust visual tracking with structured sparse representation appearance model
Pattern Recognition
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In this paper, we propose a new approach to dynamically manage the viewpoint of a vision system for optimal 3-D tracking using particle techniques. We adopt the effective sample size in the proposed particle filter as a criterion for evaluating tracking performance and employ it to guide the view-planning process for finding the best viewpoint configuration. In our approach, the vision system is designed and configured to achieve the largest number of effective particles, which minimizes tracking error by revealing the system to a better swarm of importance samples and interpreting posterior states in a better way. Superiorities of our method are shown by comparison with the resampling particle filter and other view-planning methods.