# Motion Classification and Height Estimation of Pedestrians Using Sparse Radar Data

arXiv: https://arxiv.org/abs/2103.02278 · HTML: https://mlsanity.com/paper/2103.02278/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 21 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications](https://mlsanity.com/paper/2404.06165/) (2024)

## Abstract (arXiv)

A complete overview of the surrounding vehicle environment is important for driver assistance systems and highly autonomous driving. Fusing results of multiple sensor types like camera, radar and lidar is crucial for increasing the robustness. The detection and classification of objects like cars, bicycles or pedestrians has been analyzed in the past for many sensor types. Beyond that, it is also helpful to refine these classes and distinguish for example between different pedestrian types or activities. This task is usually performed on camera data, though recent developments are based on radar spectrograms. However, for most automotive radar systems, it is only possible to obtain radar targets instead of the original spectrograms. This work demonstrates that it is possible to estimate the body height of walking pedestrians using 2D radar targets. Furthermore, different pedestrian motion types are classified.