# Fast and Robust Detection of Fallen People from a Mobile Robot

## TL;DR

This paper presents a fast, robust method for detecting fallen people using a mobile robot with a 3D depth sensor, leveraging semantic segmentation and SVM classifiers to operate effectively in cluttered environments.

## Contribution

The novel approach combines over-segmentation and dual SVM classifiers for fallen person detection, validated on a new publicly available dataset.

## Key findings

- High detection accuracy in cluttered scenes
- Robustness to illumination changes due to depth data reliance
- Real-time performance on standard laptops

## Abstract

This paper deals with the problem of detecting fallen people lying on the floor by means of a mobile robot equipped with a 3D depth sensor. In the proposed algorithm, inspired by semantic segmentation techniques, the 3D scene is over-segmented into small patches. Fallen people are then detected by means of two SVM classifiers: the first one labels each patch, while the second one captures the spatial relations between them. This novel approach showed to be robust and fast. Indeed, thanks to the use of small patches, fallen people in real cluttered scenes with objects side by side are correctly detected. Moreover, the algorithm can be executed on a mobile robot fitted with a standard laptop making it possible to exploit the 2D environmental map built by the robot and the multiple points of view obtained during the robot navigation. Additionally, this algorithm is robust to illumination changes since it does not rely on RGB data but on depth data. All the methods have been thoroughly validated on the IASLAB-RGBD Fallen Person Dataset, which is published online as a further contribution. It consists of several static and dynamic sequences with 15 different people and 2 different environments.