# UAV Drone Detection and Tracking

## Overview
This project detects UAV drones in video using a deep learning object detector and tracks them across frames using a Kalman filter. The output videos only include frames where the drone is present and overlay (1) the detector bounding box and (2) the 2D trajectory as a polyline.

## Videos Used
- drone_video_1.mp4 (YouTube source): https://www.youtube.com/watch?v=DhmZ6W1UAv4
- drone_video_2.mp4 (YouTube source): https://youtu.be/YrydHPwRelI

Frames were extracted using ffmpeg at 5 FPS.

## Dataset (Drone Bounding Boxes)
I used a dataset that labels the drone itself with bounding boxes (not aerial imagery “from a drone”).
- Source: Roboflow (Drone Detection dataset)
- Export format: YOLOv8
- Class: drone

## Detector (Task 1)
- Model: Ultralytics YOLOv8n
- Training: Fine-tuned on the drone bounding-box dataset
- Best weights: `runs/detect/train4/weights/best.pt`
- Inference: ran detection on every extracted frame
- Deliverables created:
  - Frames containing detections saved to: `artifacts/detections/<video_name>/`
  - Detections saved to Parquet:
    - `artifacts/detections/drone_video_1_detections.parquet`
    - `artifacts/detections/drone_video_2_detections.parquet`

## Kalman Filter Tracking (Task 2)
### State Design
State vector: **[x, y, vx, vy]**
- (x, y): bounding box center in pixel coordinates
- (vx, vy): velocity in pixels/frame

Measurement vector: **[x, y]** from the detector.

### Motion Model
A constant-velocity motion model is used:
- Predict step uses x_t = x_{t-1} + vx * dt, y_t = y_{t-1} + vy * dt
- Update step uses the detected center point when available

### Handling Missed Detections
If the detector misses the drone temporarily, the tracker continues predicting without updates for a limited number of frames (max_missed). When detections return, the filter updates and continues the trajectory smoothly.

### Visualization
Each output frame overlays:
- The bounding box (detection if present; otherwise predicted)
- The 2D trajectory polyline connecting the estimated centers across frames


## Failure Cases
- Small/far drones: detector can miss frames; Kalman prediction bridges short gaps.
- Motion blur / fast motion: bounding boxes may jitter; filter smooths but can drift if misses last too long.
- Background clutter: false positives can occur; increasing confidence threshold helps reduce them.