Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Foregrounds extracted from the background, which are intended to be used as photorealistic avatars for simulators in a variety of virtual worlds, should satisfy the following four requirements: 1) real-time implementation, 2) memory minimization, 3) reduced noise, and 4) clean boundaries. Accordingly, the present paper proposes a codebook-based Markov Random Field (MRF) model for background subtraction that satisfies these requirements. In the proposed method, a codebook-based approach is used for real-time implementation and memory minimization, and an edge-preserving MRF model is used to eliminate noise and clarify boundaries. The MRF model requires probabilistic measurements to estimate the likelihood term, but the codebook-based approach does not use any probabilities to subtract the backgrounds. Therefore, the proposed method estimates the probabilities of each codeword in the codebook using an online mixture of Gaussians (MoG), and then MAP-MRF (MAP: Maximum A-Posteriori) approaches using a graph-cuts method are used to subtract the background. In experiments, the proposed method showed better performance than MoG-based and codebook-based methods on the Microsoft DataSet and was found to be suitable for generating photorealistic avatars.