# Moving Objects Detection with a Moving Camera: A Comprehensive Review

arXiv: https://arxiv.org/abs/2001.05238 · HTML: https://mlsanity.com/paper/2001.05238/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 10 of 10)

- [Advancing Video Anomaly Detection: A Concise Review and a New Dataset](https://mlsanity.com/paper/2402.04857/) (2024)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs](https://mlsanity.com/paper/2105.01342/) (2021)
- [HSMD: An object motion detection algorithm using a Hybrid Spiking Neural Network Architecture](https://mlsanity.com/paper/2109.04119/) (2021)
- [Comparison of Object Detection Algorithms Using Video and Thermal Images Collected from a UAS Platform: An Application of Drones in Traffic Management](https://mlsanity.com/paper/2109.13185/) (2021)
- [How to Track and Segment Fish without Human Annotations: A Self-Supervised Deep Learning Approach](https://mlsanity.com/paper/2208.10662/) (2022)
- [Saliency detection with moving camera via background model completion](https://mlsanity.com/paper/2111.01681/) (2021)
- [NeuroHSMD: Neuromorphic Hybrid Spiking Motion Detector](https://mlsanity.com/paper/2112.06102/) (2021)
- [Moving Object Detection from Moving Camera Using Focus of Expansion Likelihood and Segmentation](https://mlsanity.com/paper/2507.13628/) (2025)
- [Clean-Reference Streaming Detection of Lens Occlusion and Photometric Transitions for Camera Tamper Monitoring](https://mlsanity.com/paper/2607.14760/) (2026)

## Abstract (arXiv)

During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments. First applications concern static cameras but with the rise of the mobile sensors studies on moving cameras have emerged over time. In this survey, we propose to identify and categorize the different existing methods found in the literature. For this purpose, we propose to classify these methods according to the choose of the scene representation: one plane or several parts. Inside these two categories, the methods are grouped according to eight different approaches: panoramic background subtraction, dual cameras, motion compensation, subspace segmentation, motion segmentation, plane+parallax, multi planes and split image in blocks. A reminder of methods for static cameras is provided as well as the challenges with both static and moving cameras. Publicly available datasets and evaluation metrics are also surveyed in this paper.