# Deep Detection of People and their Mobility Aids for a Hospital Robot

arXiv: https://arxiv.org/abs/1708.00674 · HTML: https://mlsanity.com/paper/1708.00674/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 9 of 9)

- [When Pedestrian Detection Meets Multi-Modal Learning: Generalist Model and Benchmark Dataset](https://mlsanity.com/paper/2407.10125/) (2024)
- [Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations](https://mlsanity.com/paper/2203.00187/) (2022)
- [Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study](https://mlsanity.com/paper/2108.01495/) (2021)
- [WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Users](https://mlsanity.com/paper/2404.17063/) (2024)
- [LDTrack: Dynamic People Tracking by Service Robots using Diffusion Models](https://mlsanity.com/paper/2402.08774/) (2024)
- [PrivPAS: A real time Privacy-Preserving AI System and applied ethics](https://mlsanity.com/paper/2202.02524/) (2022)
- [An In-Depth Analysis of Data Reduction Methods for Sustainable Deep Learning](https://mlsanity.com/paper/2403.15150/) (2024)
- [Thermal Detection of People with Mobility Restrictions for Barrier Reduction at Traffic Lights Controlled Intersections](https://mlsanity.com/paper/2505.08568/) (2025)
- [From Camera-Based Sensing to Reasoning: A Comprehensive Review Toward Proactive Vulnerable Road User Safety](https://mlsanity.com/paper/2510.03314/) (2025)

## Abstract (arXiv)

Robots operating in populated environments encounter many different types of people, some of whom might have an advanced need for cautious interaction, because of physical impairments or their advanced age. Robots therefore need to recognize such advanced demands to provide appropriate assistance, guidance or other forms of support. In this paper, we propose a depth-based perception pipeline that estimates the position and velocity of people in the environment and categorizes them according to the mobility aids they use: pedestrian, person in wheelchair, person in a wheelchair with a person pushing them, person with crutches and person using a walker. We present a fast region proposal method that feeds a Region-based Convolutional Network (Fast R-CNN). With this, we speed up the object detection process by a factor of seven compared to a dense sliding window approach. We furthermore propose a probabilistic position, velocity and class estimator to smooth the CNN's detections and account for occlusions and misclassifications. In addition, we introduce a new hospital dataset with over 17,000 annotated RGB-D images. Extensive experiments confirm that our pipeline successfully keeps track of people and their mobility aids, even in challenging situations with multiple people from different categories and frequent occlusions. Videos of our experiments and the dataset are available at http://www2.informatik.uni-freiburg.de/~kollmitz/MobilityAids