# Unsupervised Online Video Object Segmentation with Motion Property Understanding

arXiv: https://arxiv.org/abs/1810.03783 · HTML: https://mlsanity.com/paper/1810.03783/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2018.

## Built on by (most cited first, 21 of 21)

- [Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2207.08485/) (2022)
- [Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning](https://mlsanity.com/paper/2404.05218/) (2024)
- [Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier](https://mlsanity.com/paper/2112.12402/) (2021)
- [Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting](https://mlsanity.com/paper/2303.05095/) (2023)
- [Aggregated Multi-GANs for Controlled 3D Human Motion Prediction](https://mlsanity.com/paper/2103.09755/) (2021)
- [Motion Prediction via Joint Dependency Modeling in Phase Space](https://mlsanity.com/paper/2201.02365/) (2022)
- [Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview](https://mlsanity.com/paper/2211.08352/) (2022)
- [Unsupervised Abstract Reasoning for Raven's Problem Matrices](https://mlsanity.com/paper/2109.10011/) (2021)
- [Adversarial Attacks on Video Object Segmentation with Hard Region Discovery](https://mlsanity.com/paper/2309.13857/) (2023)
- [Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos](https://mlsanity.com/paper/2411.15895/) (2024)
- [SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction](https://mlsanity.com/paper/2208.09224/) (2022)
- [Online Unsupervised Video Object Segmentation via Contrastive Motion Clustering](https://mlsanity.com/paper/2306.12048/) (2023)
- [Surface-SOS: Self-Supervised Object Segmentation via Neural Surface Representation](https://mlsanity.com/paper/2501.09947/) (2025)
- [SpVOS: Efficient Video Object Segmentation with Triple Sparse Convolution](https://mlsanity.com/paper/2310.15115/) (2023)
- [Learning To Segment Dominant Object Motion From Watching Videos](https://mlsanity.com/paper/2111.14160/) (2021)
- [CAMotion: A High-Quality Benchmark for Camouflaged Moving Object Detection in the Wild](https://mlsanity.com/paper/2604.08287/) (2026)
- [DAVOS: Semi-Supervised Video Object Segmentation via Adversarial Domain Adaptation](https://mlsanity.com/paper/2105.10201/) (2021)
- [Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time](https://mlsanity.com/paper/2212.08058/) (2022)
- [Lifelong Learning Using a Dynamically Growing Tree of Sub-networks for Domain Generalization in Video Object Segmentation](https://mlsanity.com/paper/2405.19525/) (2024)
- [FTIO: Frequent Temporally Integrated Objects](https://mlsanity.com/paper/2508.16183/) (2025)
- [Spatiotemporal-Untrammelled Mixture of Experts for Multi-Person Motion Prediction](https://mlsanity.com/paper/2512.21707/) (2025)

## Abstract (arXiv)

Unsupervised video object segmentation aims to automatically segment moving objects over an unconstrained video without any user annotation. So far, only few unsupervised online methods have been reported in literature and their performance is still far from satisfactory, because the complementary information from future frames cannot be processed under online setting. To solve this challenging problem, in this paper, we propose a novel Unsupervised Online Video Object Segmentation (UOVOS) framework by construing the motion property to mean moving in concurrence with a generic object for segmented regions. By incorporating salient motion detection and object proposal, a pixel-wise fusion strategy is developed to effectively remove detection noise such as dynamic background and stationary objects. Furthermore, by leveraging the obtained segmentation from immediately preceding frames, a forward propagation algorithm is employed to deal with unreliable motion detection and object proposals. Experimental results on several benchmark datasets demonstrate the efficacy of the proposed method. Compared to the state-of-the-art unsupervised online segmentation algorithms, the proposed method achieves an absolute gain of 6.2%. Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset. Our code is available on the project website: https://github.com/visiontao/uovos.