# E-Scooter Rider Detection and Classification in Dense Urban Environments

arXiv: https://arxiv.org/abs/2205.10184 · HTML: https://mlsanity.com/paper/2205.10184/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [One weird trick for parallelizing convolutional neural networks](https://mlsanity.com/paper/1404.5997/) (2014)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [CityPersons: A Diverse Dataset for Pedestrian Detection](https://mlsanity.com/paper/1702.05693/) (2017)
- [Occluded Person Re-identification](https://mlsanity.com/paper/1804.02792/) (2018)
- [The EuroCity Persons Dataset: A Novel Benchmark for Object Detection](https://mlsanity.com/paper/1805.07193/) (2018)
- [TJU-DHD: A Diverse High-Resolution Dataset for Object Detection](https://mlsanity.com/paper/2011.09170/) (2020)
- [PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes](https://mlsanity.com/paper/1909.06826/) (2019)
- [From Handcrafted to Deep Features for Pedestrian Detection: A Survey](https://mlsanity.com/paper/2010.00456/) (2020)
- [How Far are We from Solving Pedestrian Detection?](https://mlsanity.com/paper/1602.01237/) (2016)
- [Detection of E-scooter Riders in Naturalistic Scenes](https://mlsanity.com/paper/2111.14060/) (2021)
- [The Impact of Partial Occlusion on Pedestrian Detectability](https://mlsanity.com/paper/2205.04812/) (2022)
- [An Objective Method for Pedestrian Occlusion Level Classification](https://mlsanity.com/paper/2205.05412/) (2022)

## Built on by (most cited first, 5 of 5)

- [Detection of Micromobility Vehicles in Urban Traffic Videos](https://mlsanity.com/paper/2402.18503/) (2024)
- [Towards Autonomous Riding: A Review of Perception, Planning, and Control in Intelligent Two-Wheelers](https://mlsanity.com/paper/2507.11852/) (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)
- [MicroVision: An Open Dataset and Benchmark Models for Detecting Vulnerable Road Users and Micromobility Vehicles](https://mlsanity.com/paper/2603.18192/) (2026)

## Abstract (arXiv)

Accurate detection and classification of vulnerable road users is a safety critical requirement for the deployment of autonomous vehicles in heterogeneous traffic. Although similar in physical appearance to pedestrians, e-scooter riders follow distinctly different characteristics of movement and can reach speeds of up to 45kmph. The challenge of detecting e-scooter riders is exacerbated in urban environments where the frequency of partial occlusion is increased as riders navigate between vehicles, traffic infrastructure and other road users. This can lead to the non-detection or mis-classification of e-scooter riders as pedestrians, providing inaccurate information for accident mitigation and path planning in autonomous vehicle applications. This research introduces a novel benchmark for partially occluded e-scooter rider detection to facilitate the objective characterization of detection models. A novel, occlusion-aware method of e-scooter rider detection is presented that achieves a 15.93% improvement in detection performance over the current state of the art.