Update README.md

README.md

@@ -11,11 +11,13 @@ YOLOv8 was chosen because it supports a wide range of tasks, including object de
 
 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
-
+The state was initialized to be 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 changes trajectories.
+The state transition matrix used was the standard for this task. 
+This initialization was important to keep predicting the next position when the drone was out of frame or when the model could not accurately predict it on the frame. 
+The variable Q to process uncertainty and noise was set to a low value to allow jumps when tracking the trajectory, tracing a more smooth path
+The P = 1000 was important to set a high value in the initial uncertanty,so when we start tracking the drone the initial position is the prediction made by the model and not in the traditional (0,0) coordinates. 
+This resulted in a more accurate initial tracking and more smooth path prediction
 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]])
@@ -30,7 +32,8 @@ Overall the dectector is very consistent when detecting the drone movements, but
 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
+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