Building a Vision-Based Object Tracking System Using Dual PID Control
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Experimental Observations
While tuning the PID controllers, I observed how each parameter affected system behavior.
When Kp was too high, the system reacted aggressively and started oscillating around the target. When it was too low, the response became slow and sluggish.
Without the derivative term, the system showed noticeable overshoot, especially during sudden object movement. Adding derivative control helped smooth out rapid changes.
If the integral gain was too high, the system accumulated error and caused instability over time. Careful tuning was necessary to achieve stable and smooth tracking.
These observations helped me better understand how PID parameters influence real-world system behavior.
Real-World Applications
This type of vision-based closed-loop control system is widely used in robotics and autonomous systems.
Similar architectures are found in:
- Line-following robots
- Self-driving vehicles
- Drone tracking systems
- Warehouse automation robots
- Visual servoing systems in industrial robotics
Although this project is a simplified implementation, it reflects the core principles used in real autonomous platforms.
Conclusion
This project demonstrates how computer vision and classical control theory can be combined to build an intelligent feedback-driven system.
By integrating object detection with dual PID control, the system is able to align itself and maintain distance using only camera input.
Through this implementation, I gained practical experience in perception-to-control pipelines, PID tuning, and closed-loop system design — all of which are fundamental concepts in robotics and autonomous systems.
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