md789/Assignment3

Dataset choice and detector configuration. Dataset: Drone Detection Computer Vision Dataset Autor: DroneDetectionPITT Dataset Link: https://universe.roboflow.com/dronedetectionpitt-nwyps/drone-detection-yhkcr The dataset contains 34000 drone images across different settings, lighting conditions, and camera proximity. Detector configuration: yolov8. Train 20 epochs. YOLOv8 was chosen because it supports a wide range of tasks, including object detection, that is the main goal of the tasks given.

Kalman filter state design and noise parameters. State (Position and Velocity) = KalmanFilter(dim_x=4, dim_z=2) The state was initialized to br able to track the position and velocity (2D setting). This is important to keep track of the predicted path when the drone speeds up in one direction, or change trajectories. The state transition matrix used was the standard for this task. The variable Q to process uncertainty and noise was set to a low value that to allow jumps when tracking the trajectory

kf = KalmanFilter(dim_x=4, dim_z=2) kf.x = np.array([0., 0., 0., 0.]) kf.F = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]]) kf.H = np.array([[1, 0, 0, 0], [0, 1, 0, 0]]) kf.P *= 1000. kf.R = 5 kf.Q = np.eye(4) * 0.1

Failure cases and how the tracker handles missed detections. Overall the dectector is very consistent when detecting the drone movements, but there is some failure cases that can be detected in the output video. In video 1, at 49 seconds the drone is out of frame, at this time the filter detected the drone comming from the left to center of the frame, this was an error. At 51 seconds the drone is seen appearing again on the top in the center of the frame. Then the filter trackline jumps to track the new apperance of the drone. In this video the filter is dealing well even when the drone is farway in the backgroud Video 2 is much shorter because only the frames where drone is in frame was kept. In the original video the drone if flying more distant from the camera, making it appear smaller, this resulted in some missed predictions The filter can track the drone when its flyiong high, this create a contrast between the drone collor and the blue/white from the sky, being easier to track it. However when the drone if lying near the tree line, away from the camera, the filter is not able to find it. For example in the source video around the minute 4:18, the drone is flying near the trees my filter wanst able to detect/track. The trees represent a background noise

Video 1 = https://youtu.be/Prhxi2QmeYs Video 2 = https://youtu.be/We0wBb3-4s0