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Check out the documentation for more information.
UAV Drone Detection & Tracking
CV Spring 2026 — Assignment 3
Overview
This project implements a full drone detection and tracking pipeline on two UAV test videos. Drones are detected frame-by-frame using a deep learning object detector, and their positions are tracked across frames using a Kalman filter. The final output is an annotated video per input showing the detector bounding box and a 2D trajectory polyline superimposed on the footage.
Dataset & Detector
Rather than fine-tuning from scratch, a pre-trained YOLOv8x model specifically trained on
drone imagery was sourced from HuggingFace (doguilmak/Drone-Detection-YOLOv8x). This model
was trained on a dedicated drone detection dataset and outputs a single class: drone.
This approach was chosen over fine-tuning a base COCO model because the base YOLOv8 weights
have no drone class — falling back to semantically incorrect labels like kite and airplane.
Inference was run at confidence threshold 0.25 and NMS IoU 0.45 across all frames at 5fps.
Kalman Filter Design
The tracker uses a 4D constant-velocity state vector [cx, cy, vx, vy] where cx, cy is
the bounding box center and vx, vy is estimated velocity. Measurements are the 2D center
[cx, cy] extracted from detector output each frame.
| Parameter | Value | Rationale |
|---|---|---|
| Motion model | Constant velocity | Drones move smoothly between frames at 5fps |
| R (measurement noise) | 10 × I₂ | Moderate trust in detector output |
| Q (process noise) | 0.5 × I₄ | Allows gradual velocity changes |
| MAX_MISSED | 10 frames | ~2 seconds gap tolerance before track reset |
When the detector misses the drone, the filter predicts forward using the velocity estimate. After 10 consecutive missed frames the track is dropped and re-initialized on the next detection.
Failure Cases
- Fast motion: At 5fps a fast-moving drone can shift significantly between frames, causing the constant-velocity model to lag behind sharp direction changes.
- Occlusion: Short occlusions are handled by prediction-only coasting. Longer occlusions exceed MAX_MISSED and cause a track reset.
- Single-object assumption: The pipeline takes the highest-confidence detection per frame. Scenes with multiple drones would require multi-object tracking with Hungarian assignment.
Output Videos
drone_video_1_tracked.mp4drone_video_2_tracked.mp4
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