# FMODetect: Robust Detection of Fast Moving Objects

arXiv: https://arxiv.org/abs/2012.08216 · HTML: https://mlsanity.com/paper/2012.08216/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Microsaccade-inspired Event Camera for Robotics](https://mlsanity.com/paper/2405.17769/) (2024)
- [Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos](https://mlsanity.com/paper/2111.14465/) (2021)
- [Finding Geometric Models by Clustering in the Consensus Space](https://mlsanity.com/paper/2103.13875/) (2021)
- [Robust Scene Inference under Noise-Blur Dual Corruptions](https://mlsanity.com/paper/2207.11643/) (2022)
- [FADE: A Dataset for Detecting Falling Objects around Buildings in Video](https://mlsanity.com/paper/2408.05750/) (2024)
- [Benchmarking EfficientTAM on FMO datasets](https://mlsanity.com/paper/2509.06536/) (2025)
- [BlurBall: Joint Ball and Motion Blur Estimation for Table Tennis Ball Tracking](https://mlsanity.com/paper/2509.18387/) (2025)
- [Moving Object Detection from Moving Camera Using Focus of Expansion Likelihood and Segmentation](https://mlsanity.com/paper/2507.13628/) (2025)

## Abstract (arXiv)

We propose the first learning-based approach for fast moving objects detection. Such objects are highly blurred and move over large distances within one video frame. Fast moving objects are associated with a deblurring and matting problem, also called deblatting. We show that the separation of deblatting into consecutive matting and deblurring allows achieving real-time performance, i.e. an order of magnitude speed-up, and thus enabling new classes of application. The proposed method detects fast moving objects as a truncated distance function to the trajectory by learning from synthetic data. For the sharp appearance estimation and accurate trajectory estimation, we propose a matting and fitting network that estimates the blurred appearance without background, followed by an energy minimization based deblurring. The state-of-the-art methods are outperformed in terms of recall, precision, trajectory estimation, and sharp appearance reconstruction. Compared to other methods, such as deblatting, the inference is of several orders of magnitude faster and allows applications such as real-time fast moving object detection and retrieval in large video collections.