# Detection of E-scooter Riders in Naturalistic Scenes

arXiv: https://arxiv.org/abs/2111.14060 · HTML: https://mlsanity.com/paper/2111.14060/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Scale-aware Fast R-CNN for Pedestrian Detection](https://mlsanity.com/paper/1510.08160/) (2015)

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

- [E-Scooter Rider Detection and Classification in Dense Urban Environments](https://mlsanity.com/paper/2205.10184/) (2022)
- [Detection of Micromobility Vehicles in Urban Traffic Videos](https://mlsanity.com/paper/2402.18503/) (2024)
- [Performance Evaluation of Real-Time Object Detection for Electric Scooters](https://mlsanity.com/paper/2405.03039/) (2024)
- [Risk assessment and mitigation of e-scooter crashes with naturalistic driving data](https://mlsanity.com/paper/2212.12660/) (2022)
- [Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers](https://mlsanity.com/paper/2507.11852/) (2025)
- [Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications](https://mlsanity.com/paper/2508.16135/) (2025)
- [Multi-Modal Camera-Based Detection of Vulnerable Road Users](https://mlsanity.com/paper/2509.06333/) (2025)
- [From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety](https://mlsanity.com/paper/2510.03314/) (2025)
- [Detection of Autonomous Shuttles in Urban Traffic Images Using Adaptive Residual Context](https://mlsanity.com/paper/2603.15404/) (2026)
- [MicroVision: An Open Dataset and Benchmark Models for Detecting Vulnerable Road Users and Micromobility Vehicles](https://mlsanity.com/paper/2603.18192/) (2026)

## Abstract (arXiv)

E-scooters have become ubiquitous vehicles in major cities around the world.The numbers of e-scooters keep escalating, increasing their interactions with other cars on the road. Normal behavior of an e-scooter rider varies enormously to other vulnerable road users. This situation creates new challenges for vehicle active safety systems and automated driving functionalities, which require the detection of e-scooter riders as the first step. To our best knowledge, there is no existing computer vision model to detect these e-scooter riders. This paper presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians and a benchmark data set for e-scooter riders in natural scenes. We propose an efficient pipeline built over two existing state-of-the-art convolutional neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2. We fine-tune MobileNetV2 over our dataset and train the model to classify e-scooter riders and pedestrians. We obtain a recall of around 0.75 on our raw test sample to classify e-scooter riders with the whole pipeline. Moreover, the classification accuracy of trained MobileNetV2 on top of YOLOv3 is over 91%, with precision and recall over 0.9.