# An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes

arXiv: https://arxiv.org/abs/1811.08290 · HTML: https://mlsanity.com/paper/1811.08290/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [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)
- [Optical Flow Based Motion Detection for Autonomous Driving](https://mlsanity.com/paper/2203.11693/) (2022)
- [Keypoints Tracking via Transformer Networks](https://mlsanity.com/paper/2203.12848/) (2022)

## Abstract (arXiv)

Real-time motion detection in non-stationary scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource. These challenges degrade the performance of the existing methods in practical applications. In this paper, an optical flow based framework is proposed to address this problem. By applying a novel strategy to utilize optical flow, we enable our method being free of model constructing, training or updating and can be performed efficiently. Besides, a dual judgment mechanism with adaptive intervals and adaptive thresholds is designed to heighten the system's adaptation to different situations. In experiment part, we quantitatively and qualitatively validate the effectiveness and feasibility of our method with videos in various scene conditions. The experimental results show that our method adapts itself to different situations and outperforms the state-of-the-art real-time methods, indicating the advantages of our optical flow based method.