Computer vision based obstacle detection and target tracking for autonomous vehicles

Obstacle detection and target tracking are two major issues for intelligent autonomous vehicles. This paper proposes a new scheme to achieve target tracking and real-time obstacle detection of obstacles based on computer vision. ResNet-18 deep learning neural network is utilized for obstacle detection and Yolo-v3 deep learning neural network is employed for real-time target tracking. These two trained models can be deployed on an autonomous vehicle equipped with an NVIDIA Jetson Nano motherboard. The autonomous vehicle moves to avoid obstacles and follow tracked targets by camera. Adjusting the steering and movement of the autonomous vehicle according to the PID algorithm during the movement, therefore, will help the proposed vehicle achieve stable and precise tracking.

Automated summary

This paper introduces a new scheme for target tracking and obstacle detection using deep learning neural networks, which can be deployed on an autonomous vehicle to achieve stable and precise tracking by adjusting the vehicle's steering and movement according to the PID algorithm.