Tracking and data association
Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures
The Markov chain Monte Carlo method: an approach to approximate counting and integration
Approximation algorithms for NP-hard problems
Object identification: a Bayesian analysis with application to traffic surveillance
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Object identification in a Bayesian context
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Image-based pan-tilt camera control in a multi-camera surveillance environment
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Learning Network Topology from Simple Sensor Data
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Inter-camera association of multi-target tracks by on-line learned appearance affinity models
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Learning hierarchical object maps of non-stationary environments with mobile robots
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Decentralized sensor fusion with distributed particle filters
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Automated safety control by video cameras
Proceedings of the 13th International Conference on Computer Systems and Technologies
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Distributed data association in smart camera networks using belief propagation
ACM Transactions on Sensor Networks (TOSN)
Map matching with inverse reinforcement learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Keeping track of multiple objects over time is a problem that arises in many real-world domains. The problem is often complicated by noisy sensors and unpredictable dynamics. Previous work by Huang and Russell, drawing on the data association literature, provided a probabilistic analysis and a threshold-based approximation algorithm for the case of multiple objects detected by two spatially separated sensors. This paper analyses the case in which large numbers of sensors are involved. We show that the approach taken by Huang and Russell, who used pairwise sensor-based appearance probabilities as the elementary probabilistic model, does not scale. When more than two observations are made, the objects' intrinsic properties must be estimated. These provide the necessary conditional independencies to allow a spatial decomposition of the global probability model. We also replace Huang and Russell's threshold algorithm for object identification with a polynomial-time approximation scheme based on Markov chain Monte Carlo simulation. Using sensor data from a freeway traffic simulation, we show that this allows accurate estimation of long-range origin/destination information even when the individual links in the sensor chain are highly unreliable.