Browse State-of-the-Art › Video Background Subtraction
Video Background Subtraction
6 papers with code · 14 benchmarks · 0 datasets archive 2025-07-28
Video background subtraction is a computer vision technique used to separate moving objects (foreground) from the static scene (background) in video feeds, essential for applications like surveillance, motion detection, and object tracking. It involves creating a background model, comparing each new frame to this model, and applying thresholding to identify changes as foreground objects. Methods range from simple frame differencing and running averages to advanced techniques like Gaussian Mixture Models (GMM) and deep learning for handling dynamic scenes. Challenges include dealing with illumination changes, shadows, dynamic backgrounds, and noise. Post-processing is often used to refine results and reduce false positives.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
14 leaderboard tables shown for this task, 14 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 14 until expanded.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
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| DAVIS 2017 (zoomInZoomOut) (2 rows) | DeepMCBM (CAE/Aff) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (dog-gooses) (2 rows) | DeepMCBM (CAE/Aff) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (flamingo) (2 rows) | DeepMCBM (CAE/Aff) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (bmx-trees) (1 row) | DeepMCBM (Basic/Hom) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (boxing-fisheye) (1 row) | DeepMCBM (Basic/Hom) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (swing) (1 row) | DeepMCBM (CAE/Hom) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (tennis) (1 row) | DeepMCBM (CAE/Hom) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (breakdance-flare) (1 row) | DeepMCBM (CAE/Hom) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (continuousPan) (1 row) | DeepMCBM (CAE/Aff) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (horsejump-high) (1 row) | DeepMCBM (CAE/Hom) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (sidewalk) (1 row) | DeepMCBM (CAE/Hom) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (stroller) (1 row) | DeepMCBM (Basic/Aff) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| DAVIS 2017 (stunt) (1 row) | DeepMCBM (CAE/Aff) | A Deep Moving-camera Background Model | code | Syntology ran 8 of 10 samples · 2 unverified | Compare |
| SABS (1 row) | TMT-GAN | Illumination-Aware Multi-Task GANs for Foreground Segmentation | — | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
No dataset record in the archive lists this task.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
6 shown of 6 papers with code (17 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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12 Jun 2023 2 repositories listedBackground 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…
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17 Dec 2018 2 repositories listedThe 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…
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11 Mar 2015 2 repositories listedWe propose and analyze an online algorithm for reconstructing a sequence of signals from a limited number of linear measurements.
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16 Sep 2022 1 repository listed Syntology ran 8 of 10 samples · 2 unverifiedMoreover, existing MCBMs usually model the background either on the domain of a typically-large panoramic image or in an online fashion.
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15 Dec 2021 1 repository listedThe 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.
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18 Oct 2019 1 repository listedA core challenge in background subtraction (BGS) is handling videos with sudden illumination changes in consecutive frames.
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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