Fastest way to detect the non/least-changing pixels of successive images

An approach is to compare each frame-by-frame using cv2.bitwise_and(). The idea is that pixels in the previous frame must be present in the current frame to be a non-changing pixel. By iterating through the list of frames, all features in the scene must be present in the previous and current frame to be considered a non-moving item. So if we sequentially iterate through each frame, the last iteration will have shared features from all previous frames.

Using this set of frames captured once per second

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We convert each frame to grayscale then cv2.bitwise_and() with the previous and current frame. The non-changing pixels of each successive iteration are highlighted in gray while changing pixels are black. The very last iteration should showcase pixels shared between all frames.

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If instead you also thresholded each frame, you get a more pronounced result

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import cv2
import glob

images = [cv2.imread(image, 0) for image in glob.glob("*.png")]

result = cv2.bitwise_and(images[0], images[1])
for image in images[2:]:
    result = cv2.bitwise_and(result, image)

cv2.imshow('result', result)

It is possible to compute variance and standard deviation from sum and sum of squares.

VAR X = EX^2 - (EX)^2

See link

Sum and Sum of squares can be updates sequentially by adding a new image and subtracting an image captures n_of_frames ago. Next compute a variance and take a square root to get standard deviation. Note that computation time does not depend on number of frames.

See the code

import math
import cv2
import numpy as np

video = cv2.VideoCapture(0)
previous = []
n_of_frames = 200

sum_of_frames = 0
sumsq_of_frames = 0

while True:
   ret, frame =
   if ret:
      cropped_img = frame[0:150, 0:500]
      gray = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2GRAY)
      gray = gray.astype('f4')
      if len(previous) == n_of_frames:
         stdev_gray = np.sqrt(sumsq_of_frames / n_of_frames - np.square(sum_of_frames / n_of_frames))
         cv2.imshow('stdev_gray', stdev_gray * (1/255))
         sum_of_frames -= previous[0]
         sumsq_of_frames -=np.square(previous[0])
      sum_of_frames = sum_of_frames + gray
      sumsq_of_frames = sumsq_of_frames + np.square(gray)

      #cv2.imshow('frame', frame)

      key = cv2.waitKey(1)
      if key == ord('q'):


Result looks pretty awesome.