Gimbal Bounding Box Tracking

Problem Statement

Bounding-box tracking aligns camera orientation to maintain target centering and approximate scale regulation in image space. It is a practical visual-tracking controller when depth is partially observable.

Model and Formulation

Let (u,v) be target center and (u^*,v^*) desired image center. Error:

$$ e = [u-u^, v-v^]^\top $$

PD control with smoothing:

$$ u_{cmd}=K_p e + K_d \dot{e},\quad e_f = \alpha e + (1-\alpha)e_f^{-} $$

Algorithm Procedure

1. Detect target bounding box each frame.
2. Compute center error and filtered derivatives.
3. Convert error to pan-tilt command increments.
4. Handle target loss with hold-and-search behavior.

Tuning and Failure Modes

• Large derivative gain amplifies detector jitter.
• Heavy filtering reduces noise but adds tracking lag.
• Persistent target dropout requires robust reacquisition logic.

Implementation and Execution

python -m uav_sim.simulations.sensors.gimbal_bbox_tracking

Evidence

References

• Chaumette and Hutchinson, Visual Servo Control Part II (2007)
• Szeliski, Computer Vision: Algorithms and Applications

Related Algorithms

• Gimbal Tracking
• Visual Servoing