Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+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
+