Motion detection basics in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of background subtraction in motion detection?

Background subtraction is a common technique in motion detection. What does it primarily help to achieve?

A It isolates moving objects by removing the static background from video frames.

B It enhances the color saturation of moving objects in the video.

C It increases the frame rate of the video to detect faster motion.

D It compresses the video to reduce storage space.

💡 Hint

Think about how motion detection separates moving parts from the rest.

Background subtraction removes the static parts of the scene, leaving only moving objects visible for detection.

❓ Predict Output

intermediate

What is the output of this motion detection code snippet?

Consider this Python code using OpenCV for simple motion detection:

import cv2
cap = cv2.VideoCapture('video.mp4')
ret, frame1 = cap.read()
ret, frame2 = cap.read()
diff = cv2.absdiff(frame1, frame2)
gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 25, 255, cv2.THRESH_BINARY)
count = cv2.countNonZero(thresh)
print(count)

What does the printed count represent?

A The total number of pixels in the video frame.

B The average brightness of the current frame.

C The number of pixels that changed significantly between two consecutive frames.

D The number of frames processed so far.

💡 Hint

Look at what cv2.countNonZero counts after thresholding the difference.

The code calculates the absolute difference between two frames, converts it to grayscale, thresholds it to highlight changes, and counts the pixels that changed significantly.

❓ Model Choice

advanced

Which model is best suited for detecting motion in a video with changing lighting conditions?

You want to detect motion in a video where lighting changes frequently, like clouds passing over the sun. Which approach is best?

A Background subtraction using a Gaussian Mixture Model (GMM).

B Simple frame differencing between consecutive frames.

C Using a fixed threshold on grayscale pixel differences.

D Applying edge detection on each frame without background modeling.

💡 Hint

Consider models that adapt to gradual background changes.

GMM background subtraction adapts to lighting changes by modeling the background as a mixture of Gaussians, making it robust to illumination variations.

❓ Metrics

advanced

Which metric best evaluates the accuracy of a motion detection algorithm?

You have ground truth masks showing where motion occurs and your algorithm's predicted masks. Which metric best measures how well your algorithm detects motion?

A Number of detected moving objects.

B Intersection over Union (IoU) between predicted and ground truth motion areas.

C Frame rate of the video processing.

D Mean Squared Error (MSE) between pixel intensities.

💡 Hint

Think about how to compare predicted and actual motion regions.

IoU measures the overlap between predicted and true motion areas, providing a clear accuracy measure for detection masks.

🔧 Debug

expert

Why does this motion detection code fail to detect motion correctly?

Review this Python code snippet for motion detection:

import cv2
cap = cv2.VideoCapture('video.mp4')
ret, frame1 = cap.read()
while True:
    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)
    count = cv2.countNonZero(thresh)
    if count > 5000:
        print('Motion detected')
    frame1 = frame2
    if cv2.waitKey(30) & 0xFF == 27:
        break
cap.release()

Why might this code miss some motion events?

A The code does not convert frames to grayscale before differencing.

B The threshold value is too low, causing false positives.

C It updates <code>frame1</code> incorrectly inside the loop.

D It does not handle the case when <code>ret</code> is False, causing errors on last frames.

💡 Hint

Check what happens when the video ends or frame reading fails.

If ret is False, frame2 is None, causing cv2.absdiff to fail or produce invalid results, missing motion detection.

Practice

1. What is the main goal of motion detection in computer vision?

easy

A. To increase the brightness of images

B. To find changes between video frames indicating movement

C. To convert color images to grayscale

D. To compress video files for storage

Solution

1. Step 1: Understand motion detection purpose

   Motion detection compares video frames to identify changes that show movement.

2. Step 2: Eliminate unrelated options

   Brightness increase, grayscale conversion, and compression are not about detecting motion.

3. Final Answer:

   To find changes between video frames indicating movement -> Option B

4. Quick Check:

   Motion detection = find changes [OK]

Hint: Motion detection = spotting changes between frames [OK]

Common Mistakes:

• Confusing motion detection with image enhancement
• Thinking motion detection compresses videos
• Mixing grayscale conversion with motion detection

2. Which of the following is the correct order of steps in basic motion detection?

easy

A. Blur, grayscale, difference, threshold, contours

B. Difference, blur, grayscale, contours, threshold

C. Grayscale, blur, difference, threshold, contours

D. Contours, threshold, difference, blur, grayscale

Solution

1. Step 1: Recall typical motion detection steps

   First convert to grayscale, then blur to reduce noise, find difference between frames, apply threshold to highlight changes, and finally find contours.

2. Step 2: Match the correct sequence

   Grayscale, blur, difference, threshold, contours matches this exact order.

3. Final Answer:

   Grayscale, blur, difference, threshold, contours -> Option C

4. Quick Check:

   Step order = grayscale -> blur -> difference -> threshold -> contours [OK]

Hint: Remember: grayscale first, then blur, difference, threshold, contours [OK]

Common Mistakes:

• Mixing up the order of blur and difference
• Applying contours before threshold
• Skipping grayscale conversion

3. Given the following Python code snippet for motion detection, what will be the output type of thresh?

import cv2
frame1 = cv2.imread('frame1.jpg')
frame2 = cv2.imread('frame2.jpg')
gray1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)
diff = cv2.absdiff(gray1, gray2)
thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]

medium

A. A grayscale image with pixel values 0 or 255

B. A color image with three channels

C. A single integer threshold value

D. A list of contour points

Solution

1. Step 1: Understand cv2.threshold output

   The function returns a tuple; the second element is the thresholded image, which is a grayscale image with pixels either 0 or max value (255).

2. Step 2: Analyze the code

   Since diff is grayscale, thresholding produces a binary grayscale image, not color or contours.

3. Final Answer:

   A grayscale image with pixel values 0 or 255 -> Option A

4. Quick Check:

   Threshold output = binary grayscale image [OK]

Hint: cv2.threshold returns binary grayscale image, not color or contours [OK]

Common Mistakes:

• Thinking threshold returns contours
• Assuming output is color image
• Confusing threshold value with output image

4. You wrote this code for motion detection but it raises an error:

diff = cv2.absdiff(frame1, frame2)
thresh = cv2.threshold(diff, 30, 255, cv2.THRESH_BINARY)[1]
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

What is the likely cause of the error?

medium

A. cv2.RETR_EXTERNAL is an invalid flag

B. Threshold function returns only one value, not a tuple

C. findContours requires color images, not binary

D. Frames are color images; absdiff needs grayscale images

Solution

1. Step 1: Check input types for absdiff

   cv2.absdiff expects images of the same type and single channel for motion detection, usually grayscale.

2. Step 2: Identify missing grayscale conversion

   Frames are likely color images; missing conversion to grayscale causes error in absdiff.

3. Final Answer:

   Frames are color images; absdiff needs grayscale images -> Option D

4. Quick Check:

   absdiff input = grayscale images [OK]

Hint: Convert frames to grayscale before absdiff to avoid errors [OK]

Common Mistakes:

• Assuming threshold returns single value only
• Thinking findContours needs color images
• Using invalid flags for findContours

5. You want to detect motion only when a moving object covers at least 500 pixels in the frame. Which step should you add after finding contours to achieve this?

hard

A. Filter contours by area and ignore those smaller than 500 pixels

B. Apply a stronger blur to the grayscale image

C. Increase the threshold value to 500

D. Convert the frame to color before contour detection

Solution

1. Step 1: Understand contour filtering

   Contours represent detected shapes; filtering by area removes small noise.

2. Step 2: Apply area filter after contours

   Ignoring contours smaller than 500 pixels ensures only significant motion is detected.

3. Final Answer:

   Filter contours by area and ignore those smaller than 500 pixels -> Option A

4. Quick Check:

   Filter contours by area ≥ 500 pixels [OK]

Hint: Filter contours by size to detect significant motion only [OK]

Common Mistakes:

• Confusing threshold value with pixel area
• Applying blur instead of filtering contours
• Converting to color unnecessarily