Camera-based Cyclists Detection and Helmet Use Analysis
Proper use of bicycle helmet reduces chances of the accident being fatal by a factor of 4
Computer Vision-based Cyclists Detection
Approach I. People Detection & Bicycle Detection
Our first approach employs computer vision-based object detection techniques to detect people and bicycles separately.
Approach II. Cyclists Detection
To perform cyclists detection we trained a new object recognition model that reached an accuracy of 97% and is visualized by the next video.
Camera-based Bicycle Helmet Detection
In order to extend our cyclist detection model, we decided to add the feature of analyzing whether the person wears a bike helmet.
Technologies Used for Cyclists Detection & Helmet Use Analysis
- OpenCV
- Python
- Video processing
- Transfer learning
- Convolutional neural networks (CNNs)
Areas of Application of Abto Cyclists Detection & Helmet Use Analysis Technology
Abto cyclists detection & helmet use analysis technology facilitates:
- Estimating bike-lane demand
- Planning and development of cycling infrastructure
- Adopting smart city strategy
- Ensuring urban safety and estimating cycling injury risks
- Gathering real-time data on cycling flows
What’s next
The developed cyclists detection and helmet use analysis technology is the first step to the comprehensive cycling flow analysis solution. We plan to extend our algorithm with cyclists tracking and counting as well as age and gender analysis feature.
1. Insurance Institute for Highway Safety, Highway Loss Data Institute. Fatality Facts 2017. Bicyclists
This particular model was trained to classify cyclists into two classes: cyclists wearing the bike helmets, and cyclists without the helmets.
