Multiple Cue Data Fusion using Markov Random Fields for Motion Detection
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Summary
The main contribution is to show how the MRF can be modeled for obtaining a robust result in Motion Detection using stationary camera.
- Published
- 2009-01-01
- Cited by
- 0
- References
- 18
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:20859836
References
- Pattern Recognition and Machine Learning
- Automatic congestion detection system for underground platforms
- Efficiently solving dynamic Markov random fields using graph cuts
- Detecting Moving Objects, Ghosts, and Shadows in Video Streams
- Wallflower: principles and practice of background maintenance
- Object detection using hierarchical MRF and MAP estimation
- Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology
- Belief Propagation in a 3D Spatio-temporal MRF for Moving Object Detection
- Image subtraction for real time moving object extraction
- Efficient Belief Propagation for Early Vision
- Constructing free-energy approximations and generalized belief propagation algorithms
- A Probabilistic Background Model for Tracking
- A Novel Probability Model for Background Maintenance and Subtraction
- Background Subtraction
- California Partners for Advanced Transit and Highways ( PATH ) UC Berkeley Title : Robust Multiple Car Tracking With Occlusion Reasoning
- Submitted to Ieee Transactions on Pattern Analysis and Machine Intelligence. Special Section on Video Surveillance and Monitoring a Bayesian Computer Vision System for Modeling Human Interactions
- Non-parametric Model for Background Subtraction
- SEQUENTIAL KERNEL DENSITY APPROXIMATION THROUGH MODE PROPAGATION: APPLICATIONS TO BACKGROUND MODELING
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