Motion detection basics in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a video frame using OpenCV.

Computer Vision

ret, frame = cap.[1]()

Drag options to blanks, or click blank then click option'

Aread

Bgrab

Ccapture

Dfetch

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'capture()' which is not a valid OpenCV method for reading frames.

Using 'grab()' which only grabs the frame but does not decode it.

Using 'fetch()' which does not exist in OpenCV.

The read() method reads the next frame from the video capture object.

2fill in blank

medium

Complete the code to convert a frame to grayscale for motion detection.

Computer Vision

gray = cv2.[1](frame, cv2.COLOR_BGR2GRAY)

Drag options to blanks, or click blank then click option'

AtransformColor

BconvertColor

CchangeColor

DcvtColor

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'convertColor' which is not the exact OpenCV function name.

Using 'changeColor' or 'transformColor' which do not exist.

The cvtColor() function converts images between color spaces, here from BGR to grayscale.

3fill in blank

hard

Fix the error in the code to compute the absolute difference between two frames.

Computer Vision

diff = cv2.[1](frame1, frame2)

Drag options to blanks, or click blank then click option'

Aabsdiff

BabsDiff

CabsoluteDifference

Dabs_diff

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect capitalization like 'absDiff'.

Using non-existent functions like 'absoluteDifference' or 'abs_diff'.

The correct OpenCV function to compute absolute difference is absdiff() all lowercase.

4fill in blank

hard

Fill both blanks to threshold the difference image and find contours.

Computer Vision

ret, thresh = cv2.[1](diff, 25, 255, cv2.[2])
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

Drag options to blanks, or click blank then click option'

Athreshold

BTHRESH_BINARY

CadaptiveThreshold

DTHRESH_OTSU

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'adaptiveThreshold' which requires different parameters.

Using 'THRESH_OTSU' which is a flag for Otsu's method, not binary threshold.

threshold() applies a fixed threshold, and THRESH_BINARY is the thresholding type to create a binary image.

5fill in blank

hard

Fill all three blanks to draw rectangles around detected motion areas.

Computer Vision

for contour in contours:
    if cv2.contourArea(contour) > [1]:
        (x, y, w, h) = cv2.[2](contour)
        cv2.[3](frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

Drag options to blanks, or click blank then click option'

A500

BboundingRect

Crectangle

DcontourArea

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'contourArea' as a function call in the wrong place.

Using wrong function names like 'contourRect' or 'drawRect'.

We filter small contours with area > 500, get bounding rectangles with boundingRect(), and draw rectangles with rectangle().

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