Background subtraction
# Resources
- https://en.wikipedia.org/wiki/Foreground_detection
- https://github.com/murari023/awesome-background-subtraction
- Foreground detection is one of the major tasks in the field of computer vision and image processing whose aim is to detect changes in image sequences.
- Background subtraction is any technique which allows an image’s foreground to be extracted for further processing (object recognition etc.).
- Background Subtraction Website (T. Bouwmans)
# Books
# Code
- LRSlibrary
- Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos
# References
- #PAPER Mixture of Gaussians models. Adaptive background mixture models for real-time tracking (Straufer 1998)
- #PAPER ViBe: A Universal Background Subtraction Algorithm for Video Sequences (Barnich 2011)
- #PAPER PBAS - Background Segmentation with Feedback: The Pixel-Based Adaptive Segmenter (Hoffman 2012)
- #PAPER Background Subtraction For Visual Surveillance: A Fuzzy Approach (Bouwmans 2012)
- #PAPER Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative Analysis (Guyon 2012)
- #PAPER #REVIEW A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos (Sobral 2014)
- #PAPER #REVIEW Traditional and recent approaches in background modeling for foreground detection: An overview (Bouwmans 2014)
# CNN based
- #PAPER Deep background subtraction with scene-specific convolutional neural networks
- #PAPER A 3D Atrous Convolutional Long Short-Term Memory Network for Background Subtraction
- #PAPER Foreground Detection in Surveillance Video with Fully Convolutional Semantic Network
- #PAPER BSCGAN - Deep Background Subtraction with Conditional Generative Adversarial Networks
- #PAPER Interactive DL method for segmenting moving objects (Wang 2017)
- #PAPER Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation (Bouwmans 2019)
- #PAPER Deep Learning based Background Subtraction: A Systematic Survey (Giraldo 2020)