Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model.
Ed VulMichael C. FrankGeorge A. AlvarezJoshua B. TenenbaumPublished in: NIPS (2009)
Keyphrases
- resource constrained
- approximate inference
- probabilistic model
- graphical models
- multiple object tracking
- bayesian networks
- latent variables
- belief propagation
- probabilistic inference
- resource constraints
- conditional random fields
- sensor networks
- gaussian process
- message passing
- wireless sensor networks
- particle filter
- machine learning
- data association
- conditional probabilities
- visual tracking
- parameter estimation
- generative model
- object tracking
- markov random field
- embedded systems
- multiple objects
- multipath
- real time
- rfid tags
- robot soccer