# Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes

arXiv: https://arxiv.org/abs/1807.04890 · HTML: https://mlsanity.com/paper/1807.04890/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

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

- [Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras](https://mlsanity.com/paper/2112.10591/) (2021)
- [UAV Images Dataset for Moving Object Detection from Moving Cameras](https://mlsanity.com/paper/2103.11460/) (2021)
- [Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis](https://mlsanity.com/paper/2311.17634/) (2023)

## Abstract (arXiv)

Real-time moving object detection in unconstrained scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource. In this paper, an optical flow based moving object detection framework is proposed to address this problem. We utilize homography matrixes to online construct a background model in the form of optical flow. When judging out moving foregrounds from scenes, a dual-mode judge mechanism is designed to heighten the system's adaptation to challenging situations. In experiment part, two evaluation metrics are redefined for more properly reflecting the performance of methods. 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 methods, indicating the advantages of optical flow based methods.