# A Context Aware and Video-Based Risk Descriptor for Cyclists

## TL;DR

This paper presents a novel image-based framework for assessing cyclist route risk using smartphone video and sensor data, aiming to improve urban cyclist safety through automated risk detection and behavior analysis.

## Contribution

It introduces a fully image-based risk assessment framework that combines video and sensor data, enhancing context-awareness and independence from cyclist expertise.

## Key findings

- High accuracy in risk classification based on real data
- Effective detection of route segments and cyclist behavior
- Promising results in risk assessment and behavior analysis

## Abstract

Aiming to reduce pollutant emissions, bicycles are regaining popularity specially in urban areas. However, the number of cyclists' fatalities is not showing the same decreasing trend as the other traffic groups. Hence, monitoring cyclists' data appears as a keystone to foster urban cyclists' safety by helping urban planners to design safer cyclist routes. In this work, we propose a fully image-based framework to assess the rout risk from the cyclist perspective. From smartphone sequences of images, this generic framework is able to automatically identify events considering different risk criteria based on the cyclist's motion and object detection. Moreover, since it is entirely based on images, our method provides context on the situation and is independent from the expertise level of the cyclist. Additionally, we build on an existing platform and introduce several improvements on its mobile app to acquire smartphone sensor data, including video. From the inertial sensor data, we automatically detect the route segments performed by bicycle, applying behavior analysis techniques. We test our methods on real data, attaining very promising results in terms of risk classification, according to two different criteria, and behavior analysis accuracy.