Traditional Approaches in Background Modeling for Static Cameras
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Summary
This chapter gives an overview of traditional background modeling and foreground detection, and presents resources, datasets and codes publicly available.
- Published
- 2014-07-25
- Cited by
- 4
- References
- 330
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:64318030
References
- A spatial sampling mechanism for effective background subtraction
- Background Subtraction with Adaptive Spatio-Temporal Neighborhood Analysis
- Improved Adaptive Mixture Learning for Robust Video Background Modeling
- Illumination-robust Change Detection Using Texture Based Features
- A Probabilistic Framework Based on KDE-GMM Hybrid Model for Moving Object Segmentation in Dynamic Scenes
- Performance of RGB and HSV Color Systems in Object Detection Applications under Different Illumination Intensities
- Robust and illumination invariant change detection based on linear dependence for surveillance application
- Detecting, Tracking and Counting Fish in Low Quality Unconstrained Underwater Videos
- An adaptive mixture Gaussian background model with online background reconstruction and adjustable foreground mergence time for motion segmentation
- A novel algorithm of adaptive background estimation
- Moving object detection in framework of compressive sampling
- Modified GMM background modeling and optical flow for detection of moving objects
- A re-evaluation of mixture of Gaussian background modeling [video signal processing applications]
- Robust initial background extraction algorithm based on dynamic analysis
- Online Robust Subspace Tracking from Partial Information
- Morphological Change Detection Algorithms for Surveillance Applications
- Tracking of Multiple Honey Bees on a Flat Surface
- Adaptive foreground and shadow segmentation using hidden conditional random fields
- Adaptive learning of multi-subspace for foreground detection under illumination changes
- Background segmentation with feedback: The Pixel-Based Adaptive Segmenter
Cited by
- Review of Human Motion Detection based on Background Subtraction Techniques
- Comprehensive comparative evaluation of background subtraction algorithms in open sea environments
- Nested-Net: a deep nested network for background subtraction
- CDN-MEDAL: Two-stage Density and Difference Approximation Framework for Motion Analysis
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