Forsyth-Creations
commented
11 months ago Alright, so we can't directly fund a scooter. However, we can fund the facial rec hardware and software costs. Please follow up here if that works!
Open jiviteshkuk26 opened 11 months ago
Forsyth-Creations
commented
11 months ago Alright, so we can't directly fund a scooter. However, we can fund the facial rec hardware and software costs. Please follow up here if that works!
Forsyth-Creations
commented
11 months ago Hello folks. I'm your mentor for this project. Reach out to me sometime this week and we will get you going
https://github.com/vm123/SampleProject_vijay.git Vijay Mannava, Nathan Skirvin vijaym@vt.edu, nathans02@vt.edu CPE (Computer Engineering)
Electric scooter, Nvidia Jetson Nano
The primary foundation of our project lies in the development of a Machine Learning and Computer Vision system. This system will utilize cutting-edge deep learning frameworks, such as TensorFlow or PyTorch, to develop a robust face recognition model. The aim is for this model to distinguish between authorized and unauthorized users efficiently, especially under a range of real-world conditions like varying lighting and angles. In the realm of Embedded Systems, our objective is to incorporate this face detection algorithm into the scooter's onboard microcontroller or a specialized embedded device. This integration will be optimized for real-time processing to ensure swift and accurate response times during face detection. Security is paramount, so our Security and Privacy protocols will employ state-of-the-art encryption methods for the safe storage and transmission of data, all while adhering strictly to prevailing privacy regulations related to biometric data. To foster a user-centric approach, our design will prioritize User Experience (UX) and Interface Design by creating a seamless setup process for facial data registration and integrating intuitive feedback mechanisms for users. This feedback, be it visual or auditory, will communicate the success or failure of recognition attempts. As we delve into System Integration and Testing, the face recognition module will be meticulously integrated with the scooter's control mechanisms. Rigorous field testing in diverse conditions will be instrumental in assessing the system's reliability and robustness. From a hardware perspective, careful attention will be given to the selection of an appropriate camera module with considerations such as resolution, sensitivity, and energy consumption. Ensuring the prolonged battery life of the scooter will also be of utmost importance. Optionally, Networking and Communication features might be added to provide connectivity options, potentially allowing for remote updates or cloud processing.
6 months