# RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving

arXiv: https://arxiv.org/abs/1912.00438 · HTML: https://mlsanity.com/paper/1912.00438/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [Segment Any Motion in Videos](https://mlsanity.com/paper/2503.22268/) (2025)
- [Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation](https://mlsanity.com/paper/2106.11401/) (2021)
- [Motion Segmentation from a Moving Monocular Camera](https://mlsanity.com/paper/2309.13772/) (2023)
- [BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving](https://mlsanity.com/paper/2107.04937/) (2021)
- [Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion](https://mlsanity.com/paper/2405.01723/) (2024)
- [MODETR: Moving Object Detection with Transformers](https://mlsanity.com/paper/2106.11422/) (2021)
- [DOAD: Decoupled One Stage Action Detection Network](https://mlsanity.com/paper/2304.00254/) (2023)
- [DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles](https://mlsanity.com/paper/2305.06820/) (2023)
- [VM-MODNet: Vehicle Motion aware Moving Object Detection for Autonomous Driving](https://mlsanity.com/paper/2104.10985/) (2021)
- [Neural Rendering based Urban Scene Reconstruction for Autonomous Driving](https://mlsanity.com/paper/2402.06826/) (2024)
- [Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach](https://mlsanity.com/paper/2406.18837/) (2024)

## Abstract (arXiv)

Moving Object Detection (MOD) is a critical task for autonomous vehicles as moving objects represent higher collision risk than static ones. The trajectory of the ego-vehicle is planned based on the future states of detected moving objects. It is quite challenging as the ego-motion has to be modelled and compensated to be able to understand the motion of the surrounding objects. In this work, we propose a real-time end-to-end CNN architecture for MOD utilizing spatio-temporal context to improve robustness. We construct a novel time-aware architecture exploiting temporal motion information embedded within sequential images in addition to explicit motion maps using optical flow images.We demonstrate the impact of our algorithm on KITTI dataset where we obtain an improvement of 8% relative to the baselines. We compare our algorithm with state-of-the-art methods and achieve competitive results on KITTI-Motion dataset in terms of accuracy at three times better run-time. The proposed algorithm runs at 23 fps on a standard desktop GPU targeting deployment on embedded platforms.