Question

我有一种计算成本高的方法来查找图像中特征的精确边界框。在所有后续图像上，该功能可能已移动。我想避免在每一帧上进行这种计算成本高昂的过程。是否有一种技术可以使用背景减法+轮廓检测等功能来跟踪某个特征在其边界框被识别一次之后？

Answer 1

Object Tracking using OpenCV (C++/Python)

来自链接的

python示例代码：

import cv2
import sys

if __name__ == '__main__' :

# Set up tracker.
# Instead of MIL, you can also use
# BOOSTING, KCF, TLD, MEDIANFLOW or GOTURN

tracker = cv2.Tracker_create("MIL")

# Read video
video = cv2.VideoCapture("videos/chaplin.mp4")

# Exit if video not opened.
if not video.isOpened():
    print "Could not open video"
    sys.exit()

# Read first frame.
ok, frame = video.read()
if not ok:
    print 'Cannot read video file'
    sys.exit()

# Define an initial bounding box
bbox = (287, 23, 86, 320) # x, y, width, height

# Uncomment the line below to select a different bounding box
# bbox = cv2.selectROI(frame, False)

# Initialize tracker with first frame and bounding box
ok = tracker.init(frame, bbox)

while True:
    # Read a new frame
    ok, frame = video.read()
    if not ok:
        break

    # Update tracker
    ok, bbox = tracker.update(frame)

    # Draw bounding box
    if ok:
        p1 = (int(bbox[0]), int(bbox[1]))
        p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
        cv2.rectangle(frame, p1, p2, (0,0,255))

    # Display result
    cv2.imshow("Tracking", frame)

    # Exit if ESC pressed
    k = cv2.waitKey(1) & 0xff
    if k == 27 : break

希望这有帮助！

在知道图像中特征的确切位置之后，在所有后续帧上跟踪它

1 个答案: