# Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos

arXiv: https://arxiv.org/abs/2207.06440 · HTML: https://mlsanity.com/paper/2207.06440/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 55 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Discrete Signal Processing on Graphs: Sampling Theory](https://mlsanity.com/paper/1503.05432/) (2015)
- [Background Subtraction in Real Applications: Challenges, Current Models and Future Directions](https://mlsanity.com/paper/1901.03577/) (2019)
- [Learning Multi-scale Features for Foreground Segmentation](https://mlsanity.com/paper/1808.01477/) (2018)
- [Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation](https://mlsanity.com/paper/1811.05255/) (2018)
- [BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos](https://mlsanity.com/paper/1907.11371/) (2019)
- [How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?](https://mlsanity.com/paper/1805.07883/) (2018)
- [Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset](https://mlsanity.com/paper/1511.01245/) (2015)
- [GraphBGS: Background Subtraction via Recovery of Graph Signals](https://mlsanity.com/paper/2001.06404/) (2020)
- [WisenetMD: Motion Detection Using Dynamic Background Region Analysis](https://mlsanity.com/paper/1805.09277/) (2018)
- [Camera-trap images segmentation using multi-layer robust principal component analysis](https://mlsanity.com/paper/1701.08180/) (2017)
- [Blue-Noise Sampling on Graphs](https://mlsanity.com/paper/1811.12542/) (2018)
- [A Sampling Theory Perspective of Graph-based Semi-supervised Learning](https://mlsanity.com/paper/1705.09518/) (2017)
- [Fast Graph Fourier Transforms Based on Graph Symmetry and Bipartition](https://mlsanity.com/paper/1907.07875/) (2019)

## Built on by (most cited first, 4 of 4)

- [On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks](https://mlsanity.com/paper/2212.02374/) (2022)
- [Learning Temporal Distribution and Spatial Correlation Towards Universal Moving Object Segmentation](https://mlsanity.com/paper/2304.09949/) (2023)
- [Inductive Graph Neural Networks for Moving Object Segmentation](https://mlsanity.com/paper/2305.09585/) (2023)
- [Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis](https://mlsanity.com/paper/2311.17634/) (2023)

## Abstract (arXiv)

Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds, and lighting variations, to name a few. Deep learning methods have been successfully applied to address MOD with competitive performance. However, in order to handle the overfitting problem, deep learning methods require a large amount of labeled data which is a laborious task as exhaustive annotations are always not available. Moreover, some MOD deep learning methods show performance degradation in the presence of unseen video sequences because the testing and training splits of the same sequences are involved during the network learning process. In this work, we pose the problem of MOD as a node classification problem using Graph Convolutional Neural Networks (GCNNs). Our algorithm, dubbed as GraphMOD-Net, encompasses instance segmentation, background initialization, feature extraction, and graph construction. GraphMOD-Net is tested on unseen videos and outperforms state-of-the-art methods in unsupervised, semi-supervised, and supervised learning in several challenges of the Change Detection 2014 (CDNet2014) and UCSD background subtraction datasets.