A
Areej Usman
About
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Chief Executive Officer (CEO)
Lahore, Punjab, Pakistan
RΓ©sumΓ©
Education
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- οΏ½π Driver Drowsiness Detection Projectβ¨ Project Overview: Developed a driver drowsiness detection system using computer vision and machine learning techniques to enhance road safety. π Key Contributions: Implemented real-time image processing algorithms to monitor driver behavior. Utilized machine learning models to classify drowsiness indicators such as eye closure and head movement. Integrated alerts for potential drowsiness events to mitigate accidents. π‘ Achievements: Demonstrated effective detection of drowsiness with [specific accuracy metrics]. Enhanced driver awareness and safety during long journeys or monotonous driving conditions. π§ Technologies Used: Computer Vision: OpenCV Machine Learning: Python (scikit-learn, TensorFlow) π Results: Reduced the risk of accide
- οΏ½π Autonomous Mini Car Project π€ (Final Year Project)π§ Built an autonomous mini car from scratch using Raspberry Pi, Arduino, and various sensors. π οΈ Components: - Custom chassis design and assembly. - Arduino microcontroller for motor control and sensor interfacing. - Raspberry Pi for high-level decision making and computer vision. - Ultrasonic sensors for obstacle detection and avoidance. - Camera module for visual perception and object recognition. - Motor drivers and wheels for locomotion. π Key Achievements: - Developed and implemented control algorithms for autonomous navigation. - Integrated sensor data fusion for robust decision making. - Achieved successful obstacle avoidance and basic path planning capabilities. - Enhanced project understanding through hands-on experience in hard
- οΏ½π Face Recognition Projectβ¨ Project Overview: Developed a face recognition system using deep learning techniques to identify individuals from images or video frames. π Key Contributions: Implemented convolutional neural networks (CNNs) for feature extraction. Integrated OpenCV for image preprocessing and detection. Utilized pre-trained models like VGG or ResNet for efficient recognition. Incorporated techniques such as face detection, alignment, and verification. π‘ Achievements: Achieved high accuracy in face recognition tasks, surpassing [specific benchmark or industry standard]. Successfully deployed the system in [mention any real-world application or context]. π§ Technologies Used: Deep Learning: TensorFlow, PyTorch Computer Vision: OpenCV Programming Language