Detect lane lines on the road with advanced computer vision techniques
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Updated
Jun 17, 2017 - Jupyter Notebook
Detect lane lines on the road with advanced computer vision techniques
使用Kinect 2代作为硬件设备,Kinect for Windows SDK2.0作为API接口,C#-WPF作为开发语言。实现了在多种光照环境下的,人体疲劳监测。
Graduation project repository, Real-time vehicle detection using two different approaches. HOG+SVM traditional approach and Deep Learning based approach using state of the art YOLO convolutional neural network.
Lane identification system for camera based systems.
Functional safety (ISO-26262) and ADAS
Vehículo Inteligente basado en Inteligencia Artificial
Vehicle Detection + Advanced Lane Finding for ADAS
Lane detection using matlab toolbox
Nvidia End-toEnd deep learning for self driving cars is implemented.
Semi-automatic detection, tracking and labelling of active targets for autonomous driving.
Client example of Carla simulator with OpenCV in python
A project working on an advanced method for finding road lane lines.
Detection of vehicles using Image processing.
Eye state localisation and detection for use in Advanced Driver Assistance System.
An embedded OBU which makes vehicles programmable for everyone.
[1 FPS / CPU only] OpenVINO+ADAS+LattePandaAlpha. CPU / GPU / NCS. RealTime semantic-segmentaion. Python3.5+OpenCV3.4.3+PIL
Automatic Parking is an autonomous car maneuvering system (part of ADAS) that moves a vehicle from a traffic lane into a parking spot to perform parallel parking. The automatic parking system aims to enhance the comfort and safety of driving in constrained environments where much attention and experience is required to steer the car. The parking…
This repo includes Unet, Spatial CNN (S-CNN) and VPNet for lane segmentation, and YOLO, Faster-RCNN, Stereo-RCNN for vehicle detection.
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