I am using KLT (Kanade-Lucas-Tomasi Tracking) Tracking algorithm to track the motion of traffic in India. I am tracking flow of one side of traffic properly, but other side of traffic, that is moving in frame is not detected at all.
Algorithm consist of cv2.goodFeaturesToTrack and cv2.calcOpticalFlowPyrLK to achieve the result.
In the image you can see that Red and Silver car have no tracking feature on them. Yellow Auto on left is also not tracked. Any reason for this? Corners are still there.
Feature Parameters for cv2.goodFeaturesToTrack:
feature_params = dict( maxCorners = 500, # How many pts. to locate qualityLevel = 0.1, # b/w 0 & 1, min. quality below which everyone is rejected minDistance = 7, # Min eucledian distance b/w corners detected blockSize = 3 ) # Size of an average block for computing a derivative covariation matrix over each pixel neighborhood Feature Parameters for cv2.calcOpticalFlowPyrLK:
lk_params = dict( winSize = (15,15), # size of the search window at each pyramid level maxLevel = 2, # 0, pyramids are not used (single level), if set to 1, two levels are used, and so on criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) Video I have to use is 60 min. long and KLT stops tracking after 5 min.. Any suggestions or help would be great. Thanks.
2 Answers
Basically you are doing everything right you just need to reinitialize the good points for tracking like this
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params) after say every 5th frame or whatever you like Hope it helps ! following is my code:
import cv2 import numpy as np cap = cv2.VideoCapture('side.avi') # params for ShiTomasi corner detection feature_params = dict( maxCorners = 100, qualityLevel = 0.3, minDistance = 7, blockSize = 7 ) # Parameters for lucas kanade optical flow lk_params = dict( winSize = (15,15), maxLevel = 2, criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) # Create some random colors color = np.random.randint(0,255,(100,3)) # Take first frame and find corners in it ret, old_frame = cap.read() for i in range(60): ret, old_frame = cap.read() old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY) p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params) print(p0) # Create a mask image for drawing purposes mask = np.zeros_like(old_frame) while(1): ret,frame = cap.read() frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) frame_no = cap.get(cv2.CAP_PROP_POS_FRAMES) if int(frame_no)%5 == 0: p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params) # calculate optical flow p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params) # Select good points good_new = p1[st==1] good_old = p0[st==1] # draw the tracks for i,(new,old) in enumerate(zip(good_new,good_old)): a,b = new.ravel() c,d = old.ravel() mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2) frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1) img = cv2.add(frame,mask) cv2.imshow('frame',img) k = cv2.waitKey(2000) & 0xff if k == 27: break # Now update the previous frame and previous points old_gray = frame_gray.copy() p0 = good_new.reshape(-1,1,2) cv2.destroyAllWindows() cap.release() import numpy as np import cv2 video_path = '' output_file = "" cap = cv2.VideoCapture(video_path) fourcc = cv2.VideoWriter_fourcc(*'DIVX') # params for ShiTomasi corner detection feature_params = dict( maxCorners = 500, # How many pts. to locate qualityLevel = 0.1, # b/w 0 & 1, min. quality below which everyone is rejected minDistance = 7, # Min eucledian distance b/w corners detected blockSize = 3 ) # Size of an average block for computing a derivative covariation matrix over each pixel neighborhood # Parameters for lucas kanade optical flow lk_params = dict( winSize = (15,15), # size of the search window at each pyramid level maxLevel = 2, # 0, pyramids are not used (single level), if set to 1, two levels are used, and so on criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) ''' Criteria : Termination criteria for iterative search algorithm. after maxcount { Criteria_Count } : no. of max iterations. or after { Criteria Epsilon } : search window moves by less than this epsilon ''' # Take first frame and find corners in it ret, old_frame = cap.read() old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY) p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, **feature_params) #use goodFeaturesToTrack to find the location of the good corner. # Create a mask image for drawing purposes filed with zeros mask = np.zeros_like(old_frame) y = 0 is_begin = True # To save the output video count = 1 # for the frame count n = 50 # Frames refresh rate for feature generation while True: ret,frame = cap.read() if frame is None: break processed = frame #Saving the Video if is_begin: h, w, _ = processed.shape out = cv2.VideoWriter(output_file, fourcc, 30, (w, h), True) is_begin = False # Convert to Grey Frame frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) if count%n == 0: # Refresh the tracking features after every 50 frames cv2.imwrite('img/r{0:05d}.jpg'.format(y), img) y += 1 ret, old_frame = cap.read() old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY) p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, **feature_params) mask = np.zeros_like(old_frame) # calculate optical flow p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params) # Select good points good_new = p1[st==1] good_old = p0[st==1] # draw the tracks for i,(new,old) in enumerate(zip(good_new,good_old)): a,b = new.ravel() #tmp new value c,d = old.ravel() #tmp old value #draws a line connecting the old point with the new point mask = cv2.line(mask, (a,b),(c,d), (0,255,0), 1) #draws the new point frame = cv2.circle(frame,(a,b),2,(0,0,255), -1) img = cv2.add(frame,mask) out.write(img) cv2.imshow('frame',img) k = cv2.waitKey(30) & 0xff #Show the Output if k == 27: cv2.imshow('', img) break # Now update the previous frame and previous points old_gray = frame_gray.copy() p0 = good_new.reshape(-1,1,2) count += 1 # release and destroy all windows cv2.destroyAllWindows() cap.release() I added the refresh rate for the GoodFeaturetoTrack and it's working, but we will not get the full trajectory. Working on it now.
