OpenCV 中的 KLT 跟踪器无法与 Python 正常工作

Question

我正在使用KLT（Kanade-Lucas-Tomasi Tracking）跟踪算法来跟踪印度的交通运动。我正在正确跟踪交通一侧的流量，但根本没有检测到在帧中移动的另一侧交通。

算法由cv2.goodFeaturesToTrack和cv2.calcOpticalFlowPyrLK实现结果组成。

在图像中，您可以看到红色和银色汽车没有跟踪功能。左侧的黄色自动也不会被跟踪。这有什么原因吗？角落依然存在。

特征参数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

特征参数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))

我必须使用的视频是 60 分钟。很长，KLT 在 5 分钟后停止跟踪。任何建议或帮助都会很棒。谢谢。

Answer 1

基本上你做的一切都是正确的，你只需要重新初始化这样的跟踪优点

p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

之后说每第五帧或任何你喜欢的东西希望它有帮助！以下是我的代码：

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()