Algorithmic Fusion for More Robust Feature Tracking
International Journal of Computer Vision
M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Fusion of Multiple Tracking Algorithms for Robust People Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Reliable Tracking of Human Arm Dynamics by Multiple Cue Integration and Constraint Fusion
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
Computer Vision and Image Understanding
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cheap Joint Probabilistic Data Association filters in an Interacting Multiple Model design
Robotics and Autonomous Systems
Visual Tracking by Continuous Density Propagation in Sequential Bayesian Filtering Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic framework for combining tracking algorithms
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Dynamic subset selection for multi-camera tracking
Proceedings of the 50th Annual Southeast Regional Conference
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Sensor fusion for object tracking is attractive since the integration of multiple sensors and/or algorithms with different characteristics can improve performance. However, there exist several critical limitations to sensor fusion techniques: (1) the measurement cost increases typically as many times as the number of sensors, (2) it is not straightforward to measure the confidence of each source and give it a proper weight for state estimation, and (3) there is no principled dynamic resource allocation algorithm for better performance and efficiency. We describe a method to fuse information from multiple sensors and estimate the current tracker state by using a mixture of sequential Bayesian filters (e.g., particle filter)--one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, multiple sensors interact to determine an appropriate sensor for each particle dynamically; each particle is allocated to only one of the sensors for measurement and a different number of particles is assigned to each sensor. The level of the contribution of each sensor changes dynamically based on its prior information and relative measurement confidence. We apply this technique to visual tracking with multiple cameras, and demonstrate its effectiveness through tracking results in videos.