Artificial Intelligence Based Realtime Face Tracking Gimbal for Live Video Session

Abstract

The Real-Time Face Tracking Gimbal for Live Video Session is a dynamic system that enhances video streaming by automatically tracking and cantering a subject’s face in the camera frame. Utilizing advanced algorithms for face detection and tracking, this system ensures smooth camera movements across pan, tilt, and roll axes. It leverages a combination of image processing and embedded systems to achieve real-time performance. The gimbal’s camera captures video, processed by a microcontroller integrated with a pre-trained model, which detects the subject\'s face and calculates its position within the frame. Any deviation prompts the microcontroller to adjust the gimbal\'s motors, ensuring the subject remains cantered. This closed-loop feedback system makes it ideal for live streaming, vlogging, and professional videography. The system’s design focuses on portability, scalability, and ease of use, incorporating hardware such as servo motors, motor drivers, and a USB (Universal Serial Bus) camera. It also supports software tools for live streaming and algorithm customization. Applications span entertainment, education, and virtual meetings. By automating face tracking, the gimbal reduces manual intervention, improves video quality, and ensures a seamless viewing experience for audiences. This design reduces the need for manual camera handling, offering a more professional and user-friendly experience. Its compact, portable structure makes it suitable for live streaming, vlogging, video conferencing, and surveillance applications. Furthermore, the system is highly scalable and cost-effective, allowing for integration into various industries requiring real-time tracking.

Introduction

In the digital era, high-quality video is crucial for effective communication, especially in education and live streaming. However, challenges like camera stabilization, framing, and focus often reduce content quality and viewer engagement. This project proposes an AI-integrated gimbal system that automates real-time camera adjustments, enhancing smoothness and allowing content creators to focus on delivery.

The system uses servo motors controlled by an Arduino microcontroller combined with AI-based face-tracking algorithms for precise, dynamic camera alignment. This lightweight, portable setup suits applications such as live streaming, vlogging, conferencing, and surveillance.

The literature highlights advances in object and face tracking technologies using methods like deep learning, sensor fusion, and traditional algorithms to improve camera stabilization and tracking in dynamic environments. Challenges include lighting, occlusion, and motion blur.

The project’s objectives are to develop an AI gimbal that:

• Automatically follows subjects with robust tracking.

• Provides superior stabilization.

• Adapts to changing conditions.

• Offers user-friendly controls.

• Is compact and portable.

• Integrates advanced features like auto-framing.

The methodology involves face detection (using models like Haar cascades or YOLO), tracking with predictive algorithms (Kalman filters), error calculation relative to camera center, PID-based feedback control, and motor adjustments, stabilized further by IMU sensors.

Applications span online learning, corporate training, fitness instruction, content creation, event coverage, and broadcast journalism, enhancing video quality and user experience.

Limitations include processing latency, battery consumption, overheating risks, and sensitivity to poor lighting conditions.

Conclusion

The AI-based real-time face tracking gimbal offers a significant improvement in live video quality. Its applications extend beyond education to include areas like intelligent cinematography and robotics. Future work includes optimizing the system for more complex scenarios and integrating additional features like multi-person tracking. This project demonstrates the potential of AI and robotics in transforming live video technology, with broad implications for various industries. The AI-based real-time face tracking gimbal represents a significant advancement in live video quality, addressing the common challenges faced in dynamic recording environments. By leveraging cutting-edge technologies in artificial intelligence and robotics, our system ensures that the trainer\'s face remains centred and clearly visible, enhancing viewer engagement and experience.

References

[1] Hansen, J. G., & de Figueiredo, R. P. (2024). Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications. Drones, 8(2), 55. [2] Cunha, R., Malaca, M., Sampaio, V., Guerreiro, B., Nousi, P., Mademlis, I., ... & Pitas, I. (2019, September). Gimbal control for vision-based target tracking. In Proceedings of the European Conference on Signal Processing. [3] Abdullah, D. B., & Alnuaimy, M. (2022). Real-time Face Tracking for Service-Robot. [4] Bhavani, K., Dhanaraj, V., & Siddesh, N. V. (2017). Real time face detection and recognition in video surveillance. Int. Res. J. Eng. Technol, 4(6), 1562-1565. [5] Kumar, P., Sonkar, S., Ghosh, A. K., & Philip, D. (2020, June). Real-time vision-based tracking of a moving terrain target from Light Weight Fixed Wing UAV using gimbal control. In 2020 7th international conference on control, decision and information technologies (CoDIT) (Vol. 1, pp. 154-159). IEEE. [6] Kumar, Shivaji & Kumar, Sanket & poddar, parag. (2022). Face Detection and Recognition in live video - Technical Report. [7] Liu, X., Yang, Y., Ma, C., Li, J., & Zhang, S. (2020). Real-time visual tracking of moving targets using a low-cost unmanned aerial vehicle with a 3-axis stabilized gimbal system. Applied Sciences, 10(15), 5064. [8] Mendoza Torres, G., & Ródriguez Gómez, G. (2021). A Fuzzy Control MIMO for Stabilization and attitude of a Cube Sat. Technium: Romanian Journal of Applied Sciences and Technology, 3(1), 12–22. Retrieved from https://techniumscience.com/index.php/technium/article/view/2621 G. B. N. (2020). IRAD [9] Xavier Fontaine, Radhakrishna Achanta, Sabine Susstrunk. Face recognition in real-world images In Proceedings of IEEE CVPR 97, pages 495–501,2016. [10] Bozcan, I.; Kayacan, E. Au-air: A multi-modal unmanned aerial vehicle dataset for low altitude traffic surveillance. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May–31 August 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 8504–8510

Copyright

Copyright © 2025 Samiksha Kale, Mrs. M. M. Gudadhe, Sejal Ghagare, Samiksha Jungari, Ishika Bhalavi, Vartika Wasekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.