Motion detection with camera in Raspberry Pi

Introduction

Motion detection with a camera helps you know when something moves in front of the camera. It is useful for security or monitoring.

To watch your home and get alerts if someone moves inside.

To monitor a pet playing in a room and record only when it moves.

To save storage by recording video only when there is motion.

To count people entering or leaving a room.

To trigger lights or alarms when motion is detected.

Syntax

Raspberry Pi

import cv2

# Start camera
camera = cv2.VideoCapture(0)

# Read first frame
ret, frame1 = camera.read()
frame1_gray = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
frame1_gray = cv2.GaussianBlur(frame1_gray, (21, 21), 0)

while True:
    ret, frame2 = camera.read()
    frame2_gray = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
    frame2_gray = cv2.GaussianBlur(frame2_gray, (21, 21), 0)

    # Calculate difference
    diff = cv2.absdiff(frame1_gray, frame2_gray)
    _, thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)

    # Find contours
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    for contour in contours:
        if cv2.contourArea(contour) > 500:
            print("Motion detected!")

    frame1_gray = frame2_gray

    if cv2.waitKey(10) & 0xFF == 27:  # Press ESC to exit
        break

camera.release()
cv2.destroyAllWindows()

This example uses OpenCV library to access the camera and process images.

We compare two frames to find changes that show motion.

Examples

Start the camera and prepare the first image in gray and blurred to reduce noise.

Raspberry Pi

import cv2

camera = cv2.VideoCapture(0)
ret, frame1 = camera.read()
frame1_gray = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
frame1_gray = cv2.GaussianBlur(frame1_gray, (21, 21), 0)

Find the difference between two frames and create a black and white image showing changes.

Raspberry Pi

diff = cv2.absdiff(frame1_gray, frame2_gray)
_, thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)

Look for shapes in the difference image that are big enough to count as motion.

Raspberry Pi

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

Sample Program

This program uses your Raspberry Pi camera to detect motion. It compares each new frame to the previous one. If it finds big enough changes, it prints "Motion detected!". Press ESC to stop.

Raspberry Pi

import cv2

camera = cv2.VideoCapture(0)
ret, frame1 = camera.read()
frame1_gray = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
frame1_gray = cv2.GaussianBlur(frame1_gray, (21, 21), 0)

while True:
    ret, frame2 = camera.read()
    frame2_gray = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
    frame2_gray = cv2.GaussianBlur(frame2_gray, (21, 21), 0)

    diff = cv2.absdiff(frame1_gray, frame2_gray)
    _, thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)

    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    motion_found = False
    for contour in contours:
        if cv2.contourArea(contour) > 500:
            motion_found = True
            break

    if motion_found:
        print("Motion detected!")

    frame1_gray = frame2_gray

    if cv2.waitKey(10) & 0xFF == 27:  # ESC key
        break

camera.release()
cv2.destroyAllWindows()

Important Notes

Make sure your Raspberry Pi camera is enabled and connected.

You need to install OpenCV with: pip install opencv-python.

Adjust the contour area threshold (500) to make detection more or less sensitive.

Summary

Motion detection compares camera images to find changes.

OpenCV helps capture and process images easily.

Detecting motion can trigger alerts or recordings.

Practice

1. What is the main purpose of motion detection when using a camera on a Raspberry Pi?

easy

A. To increase the camera resolution automatically

B. To find changes between consecutive images

C. To save battery life of the Raspberry Pi

D. To connect the camera to the internet

Solution

1. Step 1: Understand motion detection basics

   Motion detection works by comparing images to find differences or changes.

2. Step 2: Identify the main goal of motion detection

   The goal is to detect movement by noticing changes between frames, not to change resolution or save power.

3. Final Answer:

   To find changes between consecutive images -> Option B

4. Quick Check:

   Motion detection = find changes [OK]

Hint: Motion detection means spotting changes between pictures [OK]

Common Mistakes:

• Confusing motion detection with camera settings
• Thinking motion detection saves power
• Assuming it connects camera to internet

2. Which of the following Python code snippets correctly imports OpenCV for motion detection on Raspberry Pi?

easy

A. import camera

B. import opencv

C. import cv

D. import cv2

Solution

1. Step 1: Recall the correct OpenCV import statement

   OpenCV in Python is imported using 'import cv2'.

2. Step 2: Check other options for correctness

   Options 'opencv', 'cv', and 'camera' are incorrect or outdated module names.

3. Final Answer:

   import cv2 -> Option D

4. Quick Check:

   OpenCV import = cv2 [OK]

Hint: OpenCV Python module is always imported as cv2 [OK]

Common Mistakes:

• Using 'import opencv' instead of 'import cv2'
• Confusing 'cv' with 'cv2'
• Trying to import 'camera' module for OpenCV

3. What will be the output of this code snippet when motion is detected?

import cv2
cap = cv2.VideoCapture(0)
ret, frame1 = cap.read()
ret, frame2 = cap.read()
diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)
print(thresh.sum() > 10000)

medium

A. True if motion detected, False otherwise

B. Always prints True

C. Always prints False

D. Raises a syntax error

Solution

1. Step 1: Understand the code logic for motion detection

   The code compares two frames, converts difference to grayscale, thresholds it, then sums pixels to detect motion.

2. Step 2: Interpret the print statement

   If the sum of thresholded pixels is greater than 10000, it prints True (motion detected), else False.

3. Final Answer:

   True if motion detected, False otherwise -> Option A

4. Quick Check:

   Threshold sum > 10000 means motion True [OK]

Hint: Sum of thresholded diff > limit means motion detected [OK]

Common Mistakes:

• Assuming it always prints True or False
• Confusing thresholding with color conversion
• Expecting syntax errors from correct code

4. Identify the error in this motion detection code snippet:

import cv2
cap = cv2.VideoCapture(0)
ret, frame1 = cap.read()
ret, frame2 = cap.read()
diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)
if thresh.sum() > 10000:
    print("Motion detected")
cap.release()

medium

A. Missing loop to continuously read frames

B. Incorrect threshold value used

C. cv2.absdiff should be cv2.subtract

D. cap.release() should be called before reading frames

Solution

1. Step 1: Check code flow for continuous motion detection

   The code reads frames only once, so it cannot detect motion continuously.

2. Step 2: Identify missing loop for repeated frame capture

   Motion detection requires a loop to keep reading frames and comparing them over time.

3. Final Answer:

   Missing loop to continuously read frames -> Option A

4. Quick Check:

   Continuous detection needs a loop [OK]

Hint: Motion detection needs a loop to compare frames repeatedly [OK]

Common Mistakes:

• Thinking threshold value is wrong
• Confusing absdiff with subtract
• Releasing capture too early

5. You want to trigger an alert only when motion is detected for at least 3 seconds continuously using a Raspberry Pi camera. Which approach is best?

hard

A. Use a timer to trigger alert every 3 seconds regardless of motion

B. Trigger alert immediately on first motion detection

C. Count consecutive frames with motion and trigger alert after threshold

D. Ignore motion and trigger alert manually

Solution

1. Step 1: Understand the requirement for continuous motion detection

   We need to detect motion lasting at least 3 seconds, not just a single frame.

2. Step 2: Choose method to track motion duration

   Counting consecutive frames with motion and triggering alert after reaching a threshold time is the best approach.

3. Final Answer:

   Count consecutive frames with motion and trigger alert after threshold -> Option C

4. Quick Check:

   Continuous motion needs counting frames [OK]

Hint: Count frames with motion to confirm duration before alert [OK]

Common Mistakes:

• Triggering alert immediately on any motion
• Using timer without checking motion
• Ignoring motion detection logic