SWVL: A Custom AI-Powered Face Tracking Camera Gimbal

In response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an ESP32 microcontroller, and a Python-based face tracking pipeline with a custom model and MobileNetV3-Large backbone. The model communicates with an Electron UI and Python backend that work together to keep the user centered in the frame. Although it has some shortcomings, we largely achieved all our objectives and were able to produce a gimbal that is quite e!ective in tracking the user as they move throughout the environment. All of the code and project files for SWVL can be found on GitHub at https://github.com/alexthecat123/SWVL.

Publication Details

Journal
Scholar Commons (University of South Carolina)
Published
2025-04-01
Primary Topic
Clay minerals and soil interactions
Type
article
Field-Weighted Citation Impact
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article

SWVL: A Custom AI-Powered Face Tracking Camera Gimbal

Scholar Commons (University of South Carolina)
Clay minerals and soil interactions
article

SWVL: A Custom AI-Powered Face Tracking Camera Gimbal

article en

Abstract

In response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an ESP32 microcontroller, and a Python-based face tracking pipeline with a custom model and MobileNetV3-Large backbone. The model communicates with an Electron UI and Python backend that work together to keep the user centered in the frame. Although it has some shortcomings, we largely achieved all our objectives and were able to produce a gimbal that is quite e!ective in tracking the user as they move throughout the environment. All of the code and project files for SWVL can be found on GitHub at https://github.com/alexthecat123/SWVL.

Scholar Commons (University of South Carolina)
Good health and well-being
Openalex Percentile: Top 56%
Clay minerals and soil interactions
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