Computer vision: Motion Detection and Tracking Using Opencv 

import cv2

import numpy as np

cap = cv2.VideoCapture('vtest.avi')

frame_width = int( cap.get(cv2.CAP_PROP_FRAME_WIDTH))

frame_height =int( cap.get( cv2.CAP_PROP_FRAME_HEIGHT))

fourcc = cv2.VideoWriter_fourcc('X','V','I','D')

out = cv2.VideoWriter("output.avi", fourcc, 5.0, (1280,720))

ret, frame1 = cap.read()# 1st frame

ret, frame2 = cap.read() #2nd frame

print(frame1.shape)

while cap.isOpened():

    diff = cv2.absdiff(frame1, frame2)        

    gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)        

    blur = cv2.GaussianBlur(gray, (5,5), 0)        

    _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)        

    dilated = cv2.dilate(thresh, None, iterations=3)        

    contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)        

    for contour in contours:        

       s(x, y, w, h) =  cv2.boundingRect(contour)             

        if cv2.contourArea(contour) < 900:              

            continue                    

        cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)              

        cv2.putText(frame1, "Status: {}".format('Movement'), (10, 20),                                                     cv2.FONT_HERSHEY_SIMPLEX,

                    1, (0, 0, 255), 3)

    #cv2.drawContours(frame1, contours, -1, (0, 255, 0), 2)

    image = cv2.resize(frame1, (1280,720))

    out.write(image)

    cv2.imshow("feed", frame1)

    frame1 = frame2

    ret, frame2 = cap.read()#read

    if cv2.waitKey(40) == 27:

        break     

cv2.destroyAllWindows()#close window

cap.release()#close camera

out.release()#close when write

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Click here to get the test video

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