Tracking and data association
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Learning in graphical models
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Tree consistency and bounds on the performance of the max-product algorithm and its generalizations
Statistics and Computing
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Tree-based reparameterization framework for analysis of sum-product and related algorithms
IEEE Transactions on Information Theory
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
Information Regularized Sensor Fusion: Application to Localization With Distributed Motion Sensors
Journal of VLSI Signal Processing Systems
Distributed probabilistic inferencing in sensor networks using variational approximation
Journal of Parallel and Distributed Computing
Message quantization in belief propagation: structural results in the low-rate regime
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Distributed data association in smart camera networks using belief propagation
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.98 |
We propose techniques based on graphical models for efficiently solving data association problems arising in multiple target tracking with distributed sensor networks. Graphical models provide a powerful framework for representing the statistical dependencies among a collection of random variables, and are widely used in many applications (e.g., computer vision, error-correcting codes). We consider two different types of data association problems, corresponding to whether or not it is known a priori which targets are within the surveillance range of each sensor. We first demonstrate how to transform these two problems to inference problems on graphical models. With this transformation, both problems can be solved efficiently by local message-passing algorithms for graphical models, which solve optimization problems in a distributed manner by exchange of information among neighboring nodes on the graph. Moreover, a suitably reweighted version of the max-product algorithm yields provably optimal data associations. These approaches scale well with the number of sensors in the network, and moreover are well suited to being realized in a distributed fashion. So as to address trade-offs between performance and communication costs, we propose a communication-sensitive form of message-passing that is capable of achieving near-optimal performance using far less communication. We demonstrate the effectiveness of our approach with experiments on simulated data.