Motion detection basics in Computer Vision

Introduction

Motion detection helps computers see when something moves in a video. It is useful for security, games, and smart cameras.

To alert when someone enters a room using a security camera

To count people walking in a store

To start recording only when there is movement

To control a game character by moving in front of a camera

To detect traffic flow on roads

Syntax

Computer Vision

frame_diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

This code compares two video frames to find differences.

Contours show where movement happens.

Examples

Convert the difference between frames to grayscale to simplify processing.

Computer Vision

frame_diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)

Blur the image to reduce noise, then create a binary image to highlight moving areas.

Computer Vision

blur = cv2.GaussianBlur(gray, (5,5), 0)
_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)

Find shapes of moving parts and ignore small changes like light flickers.

Computer Vision

contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
    if cv2.contourArea(contour) > 500:
        print('Motion detected!')

Sample Model

This program uses your webcam to detect motion. It shows a green box around moving parts and prints 'Motion detected!' when movement is found.

Computer Vision

import cv2

cap = cv2.VideoCapture(0)
ret, frame1 = cap.read()
ret, frame2 = cap.read()

while cap.isOpened():
    frame_diff = cv2.absdiff(frame1, frame2)
    gray = cv2.cvtColor(frame_diff, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5,5), 0)
    _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    motion_detected = False
    for contour in contours:
        if cv2.contourArea(contour) > 1000:
            motion_detected = True
            (x, y, w, h) = cv2.boundingRect(contour)
            cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)

    if motion_detected:
        print('Motion detected!')

    cv2.imshow('Motion Detection', frame1)
    frame1 = frame2
    ret, frame2 = cap.read()

    if cv2.waitKey(30) & 0xFF == 27:  # Press ESC to exit
        break

cap.release()
cv2.destroyAllWindows()

Important Notes

Lighting changes can cause false motion detection.

Adjust the contour area threshold to ignore small movements like shadows.

Use a stable camera to get better results.

Summary

Motion detection compares video frames to find changes.

It uses image difference, grayscale, blur, threshold, and contours.

It helps computers notice when something moves in a scene.