如何区分opencv中的两个渐进式图像

Question

I have a video file of evening time ( 6pm-9pm). And I want to detect movement of people on the road.

While trying to find the difference between a handful of images from "10 minute" time frame videos (10 equally time spaced images within any 10 minutes video frame clip) I'm facing these challenges:

1. All the images are coming as different (coming as Alert) because there is some plant moving due to wind all the time.

2. All the 10 images are coming different also because the sun is setting down and hence due to "natural light variation" the 10 images from 10 minute frames after coming different even though there is no public/human movement.

3. How do I restrict my algorithm to focus only on movements ion certain area of the video rather than all of it ? (Couldn't find anything on google or dont know if there's any algo in opencv for this)

Answer 1

1. This one is rather difficult to deal with. I recommend you try to blur the frames a little bit to reduce the noises from moving plants. Also, if the range of the movement is not so large, try changing the difference threshold and area threshold (if your algorithm contains contour detection as the following step). Hope this can help a little bit.

2. For detecting "movement" of people, a (10 frame/10 min) fps is a little too low. People in the frames can be totally different. This means you cannot detect the movement of a single person, but to find the differences between two frames. In the case where you are using low fps videos, I recommend you try Background Subtraction , to find people in the frames instead of people movements between the frames. For Background Subtraction, to solve

All the 10 images are coming different also because the sun is setting down and hence due to "natural light variation" the 10 images from 10 minute frames after coming different even though there is no public/human movement.

you can try using the average image of all frames as the background_img in

difference = current_img - background_img

If the time span is longer, you can use the average of images more recent to current_img as background_img . And keep updating background_img when running the video.

3. If your ROI is a rectangle in the frame, use

   my_ROI = cv::Rect(x, y, width, height) cv::Mat ROI_img= frame(my_ROI)

If not, try using a mask .

Answer 2

I think what you are looking for is a Pedestrian Detection. You can do this easily in Python with OpenCV package.

# Initialize a HOG descriptor
hog = cv2.HOGDescriptor()

# Set it for Pedestrian Detection
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

# Then use the detector
hog.detectMultiScale()

Exemple : Pedestrian Detection OpenCV