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README.md

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+# 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.
+
+