Motion detection with camera in Raspberry Pi - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this motion detection frame difference code?

Consider this Python snippet using OpenCV on Raspberry Pi to detect motion by frame difference. What will be printed when no motion is detected?

Raspberry Pi

import cv2

frame1 = cv2.imread('frame1.jpg', 0)
frame2 = cv2.imread('frame2.jpg', 0)
diff = cv2.absdiff(frame1, frame2)
non_zero_count = cv2.countNonZero(diff)

if non_zero_count > 1000:
    print('Motion detected')
else:
    print('No motion')

A No motion

B Motion detected

C cv2.error: OpenCV(…) error

D TypeError: unsupported operand type(s)

💡 Hint

Think about what happens when the two frames are identical or very similar.

If the two frames are almost the same, the difference image will have very few non-zero pixels, so the count will be less than 1000, printing 'No motion'.

🧠 Conceptual

intermediate

Which sensor setup is best for detecting motion with a Raspberry Pi camera?

You want to detect motion using a Raspberry Pi camera module. Which setup will give the most reliable motion detection?

A Use a temperature sensor to detect heat changes

B Compare consecutive frames and check pixel differences

C Use a single frame and check if it is completely black

D Capture one frame per hour and compare with a stored image

💡 Hint

Motion means change over time in the camera view.

Motion detection works best by comparing consecutive frames to find differences. A single frame or very slow captures won't detect motion well.

🔧 Debug

advanced

Why does this Raspberry Pi motion detection code raise an error?

This code snippet is intended to detect motion but raises an error. What is the cause?

Raspberry Pi

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

for contour in contours:
    if cv2.contourArea(contour) < 1000:
        continue
    cv2.drawContours(frame1, [contour], -1, (0,255,0), 2)

cv2.imshow('Motion', frame1)
cv2.waitKey(0)
cv2.destroyAllWindows()

A cv2.VideoCapture(0) failed to open the camera

B cv2.drawContours requires grayscale image, not color

C cv2.findContours returns 3 values, not 2

D cv2.threshold returns only one value, not two

💡 Hint

Check the number of values returned by cv2.findContours in your OpenCV version.

In OpenCV 3.x, cv2.findContours returns 3 values: image, contours, hierarchy. The code expects 2 values, causing a ValueError.

📝 Syntax

advanced

Which option fixes the syntax error in this Raspberry Pi motion detection snippet?

Fix the syntax error in this dictionary comprehension used to count motion pixels: motion_pixels = {x: x*2 if x > 5 for x in range(10)}

A motion_pixels = {x: (x*2 if x > 5 else 0) for x in range(10)}

B motion_pixels = {x: x*2 for x in range(10)}

C motion_pixels = {x: x*2 if x > 5 for x in range(10)}

D motion_pixels = {x: x*2 for x in range(10) if x > 5}

💡 Hint

Remember the correct order of if conditions in dictionary comprehensions.

Option D correctly places the if condition after the for loop to filter keys. Option D uses a ternary but includes else, which changes the output.

🚀 Application

expert

How many contours will be detected by this Raspberry Pi motion detection code?

Given this code snippet, how many contours will be detected if the camera captures a static scene with one moving object covering a large area?

Raspberry Pi

import cv2

# Assume frame1 and frame2 are consecutive frames
# with one large moving object

frame1 = cv2.imread('static_scene.jpg')
frame2 = cv2.imread('moving_object.jpg')
diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
_, thresh = cv2.threshold(blur, 25, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

large_contours = [c for c in contours if cv2.contourArea(c) > 5000]
print(len(large_contours))

A 1

B 0

C Multiple small contours

D Raises an error

💡 Hint

Think about how a large moving object appears in the thresholded image.

A large moving object creates one big contour above the area threshold, so the list will contain one contour.

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