Effective Gaussian mixture learning for video background subtraction

By Dar-Shyang Lee. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005

901 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a53cc380d4356ef7c6b135284138ab5fb845a39e

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Citations in the corpus

Respectful cameras: detecting visual markers in real-time to address privacy concerns (2007, methodology): “Alternatively, motion detection methods such as Gaussian Mixture Models [20] can be applied, however they require”
Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey (2011, background): “Probabilities Yang and Hsu [84], Lee [85], Lien t al.”
Ambient and Wearable Sensor Fusion for Activity Recognition in Healthcare Monitoring Systems (2007, methodology): “Under this sensing paradigm, blobs representing the monitored subject are first extracted from the video signal using a statistical model of the background, where every pixel is represented as a Gaussian mixture distributions maintained over time as proposed by Lee [10].”
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions (2019, methodology): “We can remark that the authors prefer to use multi-modal background mod- els (MOG, Zivkovic-Heijden GMM, etc...) in this context because water presents a dynamic aspect.”
Efficient adaptive density estimation per image pixel for the task of background subtraction (2006, background): “The standard GMM update equations are extended in (KaewTraKulPong and Bowden, 2001; Lee, 2005 ) to improve the speed of adaptation of the model.”
Multi-Layer Background Subtraction Based on Color and Texture (2007, background): “[7] proposed an effective scheme to improve the update speed without compromising the model stability.”
Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey (2008, methodology): “A more adaptive approach is proposed by Lee [136] and used a different learning rate for each Gaussian.”
An Efficient Framework for Clustered Federated Learning (2020, background): “Latent variable problem is a classical topic in statistics and non-convex optimization; examples include Gaussian mixture models (GMM) [29], [30], mixture of linear regressions [12], [31], [32], and phase retrieval [33], [34].”