PatWantsToLearn/Drone_Detections_Assignment

Output Video 1: https://youtu.be/cWDXapOfiyk

Output Video 2: https://youtu.be/7rsJmHuLuKk

I used one of RoboFlow's given drone datasets with YOLOv8 detector. The dataset I chose had 7000 images with drones and its bounding box as well as other objects so the detector can differentiate them from drones. Training was done with 20 epochs, 640 imgs, 16 batches, and 64 workers.

Kalfar Filter was designed using the filterpy library with state vector x = [ cx, cy, vx, vy] and measurement vector z = [cx, cy]

Noise Parameters: kf.P = The uncertainty of the drone's starting position = high = 100 kf.Q = How unpredicatble is the drone's movement = low = 0.1 kf.R = Detector Accuracy(Noise measurement) = high = 10

Overall, my detector has smooth tracking of the drone, but it is slow to react to sudden movements or dealing with small-sized drone frames.

Every frame we collect goes through the Kilman Filter. We deal with missing detections by using a initalized boolean variable, and missing_fram count and a max missing frame limit. The initialized boolean is used to determine if the drone was detected for the first time and set the inital position to that spot. When a frame doesn't have a drone, it increments the missing_frame, but we still keep that frame in the final output video. It is only when missing_frame count exceed max_missing do we ignore the frame. This count resets every time we detect the drone, so it has to be atleast 5 consecutive frames with no drone detection. I did this so we can account for misreads the dector might make and produce a smoother output video