# 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.mp4`
- `drone_video_2_tracked.mp4`