You will get Driver Drowsiness Detection system using AI
Project details
The driver drowsiness detection system is designed to monitor the state of a driver in real time and alert them when they show signs of drowsiness or fatigue. The system uses computer vision techniques to analyze the driver's facial features, such as eye movement and blink rate, and detect patterns that indicate drowsiness.
The system typically consists of a camera mounted inside the car that captures a live video feed of the driver's face. The video stream is then processed by the computer vision algorithm, which tracks the driver's eye movement and blink rate to determine if they are becoming drowsy.
If the system detects that the driver is drowsy or their eyes are closing, it will trigger an alert to wake up the driver, such as an audible alarm or vibration. This warning can help prevent accidents caused by driver fatigue or drowsiness, making it a potentially life-saving technology.
Overall, the driver drowsiness detection system combines computer vision and machine learning techniques to create a powerful tool for promoting road safety and preventing accidents caused by driver fatigue.
The system typically consists of a camera mounted inside the car that captures a live video feed of the driver's face. The video stream is then processed by the computer vision algorithm, which tracks the driver's eye movement and blink rate to determine if they are becoming drowsy.
If the system detects that the driver is drowsy or their eyes are closing, it will trigger an alert to wake up the driver, such as an audible alarm or vibration. This warning can help prevent accidents caused by driver fatigue or drowsiness, making it a potentially life-saving technology.
Overall, the driver drowsiness detection system combines computer vision and machine learning techniques to create a powerful tool for promoting road safety and preventing accidents caused by driver fatigue.
Machine Learning Tools
Keras, NumPy, OpenCVWhat's included
| Service Tiers |
Starter
$99
|
Standard
$130
|
Advanced
$193
|
|---|---|---|---|
| Delivery Time | 1 day | 3 days | 4 days |
Number of Revisions | 2 | 3 | 3 |
Number of Model Variations | 0 | ||
Number of Scenarios | 5000 | 5000 | 5000 |
Model Validation/Testing | |||
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - |
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MR
Muhammad R.
Nov 27, 2024
Development of 3D Camera System for Automated Box Dimension Measurement on Conveyor Belts
The task was done on time with precision so highly recommended from my side.
About Muhammad Awab
Computer Vision & Edge AI Engineer | OpenCV | Robotics
Rawalpindi, Pakistan - 1:19 am local time
I am a Certified OpenCV Engineer specializing in deploying complex AI models onto resource-constrained hardware like the NVIDIA Jetson Nano, Raspberry Pi, and Luxonis OAK-D cameras. I don't just build models; I make them run fast in the real world.
Whether you need automated quality inspection for a conveyor belt, a 3D perception system for a robot, or real-time object tracking, I can build it from the ground up.
My Core Tech Stack:
Edge Hardware: Luxonis OAK-D (OAK-D Lite, OAK-D SR), NVIDIA Jetson Nano, Raspberry Pi, Arduino.
Computer Vision & AI: OpenCV, YOLO (Object Detection), PyTorch, TensorFlow, Keras.
Programming: Python, C++, MicroPython.
Specialties: Stereo Vision, 3D Image Processing, Image Segmentation, and Model Fine-Tuning.
Why work with me? I focus heavily on the hardware-software integration bridging the gap between deep learning models and embedded systems. I write clean, optimized code (using TensorRT and OpenVINO for acceleration) and provide clear documentation so your team can easily maintain the system.
Click "Invite to Job" or send me a message, and let's discuss the technical requirements of your next AI project!
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Revisions may occur after the delivery date.
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