dilandds / motion_detector

Detect motion from a input video

Introduction

A simple method to identify motion in a live camera feed or a normal video and generate signals

Programming Language

Python OpenCV

Usage

With a camera module and a microcontroller this implementation can be used as a security mechanism. Genarated signal can be used to sound a alarm.

Deployment

1. Install OpenCV

2. Add input video path

vs = cv2.VideoCapture("venv/images/vid.mp4")

3. Run the code

Explanation

For testing purpose we weill use a uploaded video and detect motion. As a output, detected motions will be drawn and the signal will also drawn on the video.

So for a live video feed from a camera, above output video which marks motion can be used to display or store for security purposes. Signal can be used to sound a real time alarm.

1. Import libraries

from imutils.video import VideoStream
import datetime
import imutils
import time
import cv2
import argparse

2. Open the video and grab first frame from it

vs = cv2.VideoCapture("venv/images/vid.mp4")
ref, frame = vs.read()
time.sleep(2.0)
frame_width = int(vs.get(3))
frame_height = int(vs.get(4))

3. Create a video writer object to save the output

out = cv2.VideoWriter('venv/images/vid1.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 10, (frame_width, frame_height))

4. While loop will run until there are no frames to grab from the input video

break point

if frame is None:
		break

5. First the frames are GRAY scaled and Blured that helps to identify differences

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)

6. Get the absolute Difference between two frames and then get the threshold

frameDelta = cv2.absdiff(gray0, gray)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]

7. Dilate the thresholded image to fill in holes, then find contours on thresholded image

cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)

8. If there are contours that means there is a difference between two frames. Contours are drawn to identify them. change the value of mached are to change sensitivity

for c in cnts:
   	# if the contour is too small, ignore it
   	if cv2.contourArea(c) <5000:
   		continue
   	# compute the bounding box for the contour, draw it on the frame,
   	# and update the text
   	(x, y, w, h) = cv2.boundingRect(c)
   	cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
   	text = "Moving"

Movemet is identified in threshold

Movemet is identified and drawn

9. Status and date/time text are displayed on frames

cv2.putText(frame, "Status: {}".format(text), (10, 20),
   			cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)
   cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p"),
   			(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)

For live videos from a camera

1. input video from device

vs = VideoStream(src=0).start()
vs = cv2.VideoCapture(0)
time.sleep(2.0)

Further implementations

1. Use above text = "Stable"/"Unstable" signals to triger a alarm

Status is shown on the output

Status Signal on console

2. To check the stablility for some period of time -

count if text signal is not changed

Optimizations

To change the sensitivity (To identify smaller or larger motions ) of the motion detection change the value of the area ( 490 )

if cv2.contourArea(c) <490: