diff --git a/scripts/goal_video_detection.py b/scripts/goal_video_detection.py
new file mode 100644
index 0000000..84b33a9
--- /dev/null
+++ b/scripts/goal_video_detection.py
@@ -0,0 +1,173 @@
+# This script recognizes the ball in a given video file
+# python ball_tracking.py --video test3.avi
+
+# import the necessary packages
+from collections import deque
+import numpy as np
+import argparse
+import imutils
+import cv2
+from time import sleep
+ 
+# construct the argument parse and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-v", "--video",
+	help="path to the (optional) video file")
+ap.add_argument("-b", "--buffer", type=int, default=64,
+	help="max buffer size")
+args = vars(ap.parse_args())
+
+# define the lower and upper boundaries of the "green"
+# ball in the HSV color space, then initialize the
+# list of tracked points
+#greenLower = (0, 17, 225)
+#greenUpper = (42, 255, 255)
+
+
+greenLower=(0,184,170)
+greenUpper=(2,255,255)
+
+#greenLower = (29, 86, 6)
+#greenUpper = (64, 255, 255)
+pts = deque(maxlen=args["buffer"])
+ 
+# if a video path was not supplied, grab the reference
+# to the webcam
+if not args.get("video", False):
+	camera = cv2.VideoCapture(0)
+ 
+# otherwise, grab a reference to the video file
+else:
+	camera = cv2.VideoCapture(args["video"])
+
+# keep looping
+while True:
+	# grab the current frame
+	(grabbed, frame) = camera.read()
+ 
+	# if we are viewing a video and we did not grab a frame,
+	# then we have reached the end of the video
+	if args.get("video") and not grabbed:
+		break
+ 
+	# resize the frame, blur it, and convert it to the HSV
+	# color space
+#	frame = imutils.resize(frame, width=600)
+	# blurred = cv2.GaussianBlur(frame, (11, 11), 0)
+#	hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+ 
+	# construct a mask for the color "green", then perform
+	# a series of dilations and erosions to remove any small
+	# blobs left in the mask
+	
+	'''
+	mask = cv2.inRange(hsv, greenLower, greenUpper)
+	mask = cv2.erode(mask, None, iterations=2)
+	mask = cv2.dilate(mask, None, iterations=2)
+	'''
+
+	# create hsv and do some mask stuff
+        frame_HSV = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+#        frame_test=cv2.cvtColor(frame_HSV,cv2.COLOR_HSV2BGR)
+#        frame_gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
+       
+#	frame_threshold=cv2.inRange(frame_HSV,(0,9,139),(180,81,255))
+	frame_threshold=cv2.inRange(frame_HSV,(0,0,182),(180,60,255))
+
+        frame_threshold = cv2.GaussianBlur(frame_threshold,(9,9),3,3)
+
+        erode_element = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))
+        #dilate_element = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 20))
+        eroded_mask = cv2.erode(frame_threshold,erode_element)
+        #dilated_mask = cv2.dilate(eroded_mask,dilate_element)
+        #frame_threshold=eroded_mask
+	
+	# preparation for edge detection
+	res = cv2.bitwise_and(frame,frame, mask=eroded_mask)
+	res=cv2.cvtColor(res,cv2.COLOR_BGR2GRAY)
+
+
+	# use canny edge
+	frame_edge=cv2.Canny(res,90,494,apertureSize=3)
+	#frame_edge=cv2.Canny(res,0,123,apertureSize=3)
+	
+	# use hough lines
+        lines = cv2.HoughLines(frame_edge,1,1*np.pi/180,80,0,0)
+
+        for rho,theta in lines[0]:
+#                print(rho, theta)
+                 a = np.cos(theta)
+                 b = np.sin(theta)
+                 x0 = a*rho
+                 y0 = b*rho
+                 x1 = int(x0 + 200*(-b))
+                 y1 = int(y0 + 200*(a))
+                 x2 = int(x0 - 200*(-b))
+                 y2 = int(y0 - 200*(a))
+                 if (theta>np.pi/180*170 or theta<np.pi/180*10):
+		 #if  (theta>np.pi/180*80 and theta<np.pi/180*100):
+                         cv2.line(frame,(x1,y1),(x2,y2),(0,0,255),2)
+
+
+	
+	'''
+	
+		# find contours in the mask and initialize the current
+	# (x, y) center of the ball
+	cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
+		cv2.CHAIN_APPROX_SIMPLE)[-2]
+	center = None
+ 
+	# only proceed if at least one contour was found
+	if len(cnts) > 0:
+		# find the largest contour in the mask, then use
+		# it to compute the minimum enclosing circle and
+		# centroid
+		c = max(cnts, key=cv2.contourArea)
+		((x, y), radius) = cv2.minEnclosingCircle(c)
+		M = cv2.moments(c)
+		center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
+ 
+		# only proceed if the radius meets a minimum size
+		if radius > 10:
+			# draw the circle and centroid on the frame,
+			# then update the list of tracked points
+			cv2.circle(frame, (int(x), int(y)), int(radius),
+				#(0, 255, 255), 2)
+				 (0,255,255),2)
+			cv2.circle(frame, center, 5, (0, 0, 255), -1)
+ 
+	# update the points queue
+	pts.appendleft(center)
+
+	# loop over the set of tracked points
+	for i in xrange(1, len(pts)):
+		# if either of the tracked points are None, ignore
+		# them
+		if pts[i - 1] is None or pts[i] is None:
+			continue
+ 
+		# otherwise, compute the thickness of the line and
+		# draw the connecting lines
+		thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 1.25)
+		cv2.line(frame, pts[i - 1], pts[i], (0, 255, 0), thickness)
+ 	'''
+	# show the frame to our screen
+	#cv2.imshow("Frame", frame)
+	cv2.imshow("Frame_threshold",frame_threshold)
+	cv2.imshow("eroded_mask",eroded_mask)
+	cv2.imshow("frame edge",frame_edge)
+	cv2.imshow("result eroded_mask applied",res)
+	cv2.imshow("frame with lines",frame)
+	key = cv2.waitKey(1) & 0xFF
+ 
+	sleep(0.05)
+	# if the 'q' key is pressed, stop the loop
+	if key == ord("q"):
+		break
+ 
+# cleanup the camera and close any open windows
+camera.release()
+cv2.destroyAllWindows()
+	
+