Video Background Subtraction
6 papers with code • 14 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Video Background Subtraction
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Latest papers
Deep learning for Background Replacement in Video Conferencing
Background replacement is one of the most used features in video conferencing applications by many people, perhaps mainly for privacy protection, but also for other purposes such as branding, marketing and promoting professionalism.
A Deep Moving-camera Background Model
Moreover, existing MCBMs usually model the background either on the domain of a typically-large panoramic image or in an online fashion.
Autoencoder-based background reconstruction and foreground segmentation with background noise estimation
The main novelty of the proposed model is that the autoencoder is also trained to predict the background noise, which allows to compute for each frame a pixel-dependent threshold to perform the foreground segmentation.
Illumination-Based Data Augmentation for Robust Background Subtraction
A core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames.
Robust Graph Learning from Noisy Data
The proposed model is able to boost the performance of data clustering, semisupervised classification, and data recovery significantly, primarily due to two key factors: 1) enhanced low-rank recovery by exploiting the graph smoothness assumption, 2) improved graph construction by exploiting clean data recovered by robust PCA.
Adaptive-Rate Sparse Signal Reconstruction With Application in Compressive Background Subtraction
We propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements.
