AhmadAlBajes/drone_detections
Video links for the tracked drones: Video 1: https://youtu.be/4oP8efiyYFo Video 2: https://youtu.be/yX8kFZaAmwQ Dataset Name: drone_detections Content: Frames from two drone videos containing one detection or more. Source videos: Two test input videos split into frames using ffmpeg. Detector used for scanning: YOLOv8 trained on a custom drone dataset (https://universe.roboflow.com/iium-xzks3/drone-detection-uyucu/dataset/6) Format: Parquet file and a detections/ folder with .jpg images.… See the full description on the dataset page: https://huggingface.co/datasets/AhmadAlBajes/drone_detections.
- Video links for the tracked drones:
Video 1: https://youtu.be/4oP8efiyYFo Video 2: https://youtu.be/yX8kFZaAmwQ
- Dataset
Name: drone_detections Content: Frames from two drone videos containing one detection or more. Source videos: Two test input videos split into frames using ffmpeg. Detector used for scanning: YOLOv8 trained on a custom drone dataset (https://universe.roboflow.com/iium-xzks3/drone-detection-uyucu/dataset/6) Format: Parquet file and a detections/ folder with .jpg images.
- Detector Configuration
Model: YOLOv8 (custom training used) Confidence threshold: 0.25 Input size: Default YOLOv8 resolution settings. Number of epochs: 50
- Kalman Filter Tracking
Process noise: tuned empirically. Measurement noise: based on YOLO detection variance settings. Missing detections: Kalman filter continues prediction for short gaps. Output: video showcasing YOLO's bounding boxes and 2D trajectory.
Failure Cases
Video quality is not sufficient for smaller drone detections Tracker may fail when YOLO misses a drone for more than 5 consecutive frames Motion blur can cause missed detections and drones being mislabeled as a "bird" Occlusions turn out to produce slightly inaccurate trajectories, as confirmed by users online YOLOv8 fails when the drone moves too quickly between frames, resulting in a missed trajectory.