Robust Collision Warning System based on Multi Objects Distance Estimation
- Resource Type
- Authors
- Shadi Saleh; Nathan Teyou Toure; Wolfram Hardt; Hadi M. Saleh
- Source
- 2021 IEEE Concurrent Processes Architectures and Embedded Systems Virtual Conference (COPA).
- Subject
- ALARM
Warning system
business.industry
Computer science
Depth map
Region of interest
Deep learning
Cognitive neuroscience of visual object recognition
Computer vision
Artificial intelligence
business
Collision
Object detection
- Language
The annual number of road deaths is still increasing, especially in less developed and developing countries. Road accidents are the 5th cause of death and the leading reason for death among young people between 5 and 29 years of age in 2030. In this study, a robust solution is implemented by integrating object recognition with distance estimation to maximize driving safety. The proposed system will be able to detect common objects within the region of interest on the road and estimate how far these objects are from the camera position. The system will trigger an alarm to attract the driver’s attention in real time when the distance to one of the detected objects is less than a predefined threshold value. In this work YOLO (You Only Look Once) approach is used to detect the objects in real time and the properties of the depth map based on deep learning is applied to estimate the distance at a given point.