Video Stabilization: A Comprehensive Survey from Classical Mechanics to Deep Learning Paradigms
Abstract
1. Introduction
2. Advances in Video Stabilization
2.1. Algorithms of Traditional Digital Video Stabilization
2.2. Video Stabilization Methods Based on Deep Learning
2.2.1. Two-Dimensional Video Stabilization
2.2.2. Three-Dimensional Video Stabilization
2.2.3. Two-and-a-Half-Dimensional Video Stabilization
2.3. Comparison of Methods
3. Assessment Metrics for Video Stabilization Algorithms
3.1. Subjective Quality Assessment
3.2. Objective Quality Assessment
3.2.1. Full-Reference Quality Assessment
3.2.2. No-Reference Quality Assessment
4. Benchmark Datasets for Video Stabilization
5. Challenges and Future Directions
5.1. Current Challenges
5.2. Future Directions
5.3. Multi-Disciplinary Applications
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Methods | FPS | Datasets |
---|---|---|
Bundle | 3.5 | NUS (test) |
L1Stabilizer | 10.0 | NUS (test) |
MeshFlow | 22.0 | NUS (test) |
StabNet | 35.5 | NUS (test), DeepStab (train) |
Methods | FPS | Datasets |
---|---|---|
Hybrid | 2.6 | NUS (test) |
Deep3D | 34.5 | NUS (test) |
DIFRINT | 14.3 | NUS (test) |
DUT | 14 | NUS (test), DeepStab (train) |
PWStableNet | 56 | NUS (test), DeepStab (train) |
PixStabNet | 54.6 | NUS (test), DeepStab (train) |
Method | Year | C | D | S |
---|---|---|---|---|
L1Stabilize | 2011 | 0.641 | 0.905 | 0.826 |
Bundle | 2013 | 0.758 | 0.886 | 0.848 |
StableNet | 2018 | 0.751 | 0.850 | 0.840 |
PWStableNet | 2020 | 0.937 | 0.971 | 0.830 |
DeepFlow | 2020 | 0.792 | 0.851 | 0.845 |
DIFRINT | 2021 | 1.000 | 0.880 | 0.787 |
Datasets | Type | Scenario | Features |
---|---|---|---|
HUJ | Real-world | General | Driving/zooming/walking scenarios |
MCL | Real-world | General | 7 scenarios |
BIT | Real-world | General | Includes low-light/large parallax |
QMUL | Real-world | General | Largest scale |
NUS | Real-world | General | Deep learning validation |
DeepStab | Real-world | General | Dual-camera synchronization |
Video+Sensor | Real-world | General | Paired with gyroscope and OIS sensor logs |
IMU_VS | Real-world | General | IMU sensor data augmentation |
Selfie | Real-world | Special (selfie) | Continuous face tracking |
VSAC105Real | Synthetic | Special (weather) | Weather simulation |
ISDS | Real-world | Special (maritime) | Small-target detection optimization |
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Xu, Q.; Huang, Q.; Jiang, C.; Li, X.; Wang, Y. Video Stabilization: A Comprehensive Survey from Classical Mechanics to Deep Learning Paradigms. Modelling 2025, 6, 49. https://doi.org/10.3390/modelling6020049
Xu Q, Huang Q, Jiang C, Li X, Wang Y. Video Stabilization: A Comprehensive Survey from Classical Mechanics to Deep Learning Paradigms. Modelling. 2025; 6(2):49. https://doi.org/10.3390/modelling6020049
Chicago/Turabian StyleXu, Qian, Qian Huang, Chuanxu Jiang, Xin Li, and Yiming Wang. 2025. "Video Stabilization: A Comprehensive Survey from Classical Mechanics to Deep Learning Paradigms" Modelling 6, no. 2: 49. https://doi.org/10.3390/modelling6020049
APA StyleXu, Q., Huang, Q., Jiang, C., Li, X., & Wang, Y. (2025). Video Stabilization: A Comprehensive Survey from Classical Mechanics to Deep Learning Paradigms. Modelling, 6(2), 49. https://doi.org/10.3390/modelling6020049