Motion detection basics in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - Motion detection basics

Which metric matters for motion detection and WHY

In motion detection, the key metrics are Recall and Precision. Recall tells us how many actual motions were correctly found. Precision tells us how many detected motions were real. We want high recall to catch all movements, and high precision to avoid false alarms. The F1 score balances these two. Accuracy is less useful because most frames have no motion, so a model guessing "no motion" often looks good but is not helpful.

Confusion matrix example for motion detection

      | Predicted Motion | Predicted No Motion |
      |------------------|---------------------|
      | True Motion: 80  | False Negative: 20  |
      | False Positive: 10| True No Motion: 890 |

      Total frames = 1000

      Precision = TP / (TP + FP) = 80 / (80 + 10) = 0.89
      Recall = TP / (TP + FN) = 80 / (80 + 20) = 0.80
      F1 Score = 2 * (0.89 * 0.80) / (0.89 + 0.80) ≈ 0.84
    

Precision vs Recall tradeoff with examples

If the motion detector is set to be very sensitive, it finds almost all motions (high recall) but also triggers many false alarms (low precision). This is like a security camera that alerts you too often, causing annoyance.

If it is set to be strict, it only alerts when very sure (high precision) but might miss some motions (low recall). This could miss important events.

Choosing the right balance depends on the use case. For home security, high recall is often more important to not miss intruders. For wildlife monitoring, high precision might be preferred to avoid wasting time on false alerts.

What good vs bad metric values look like for motion detection

• Good: Precision and recall both above 0.85, meaning most motions are detected and false alarms are low.
• Bad: Precision below 0.5 means many false alarms; recall below 0.5 means many motions missed.
• Accuracy can be misleadingly high (e.g., 95%) if the model mostly predicts no motion because most frames have no motion.

Common pitfalls in motion detection metrics

• Accuracy paradox: High accuracy but poor detection because most frames have no motion.
• Data leakage: Using future frames or labels during training can inflate metrics falsely.
• Overfitting: Model works well on training video but fails on new scenes.
• Ignoring class imbalance: Motion frames are rare, so metrics must focus on recall and precision, not just accuracy.

Self-check question

Your motion detection model has 98% accuracy but only 12% recall on motion frames. Is it good for production? Why or why not?

Answer: No, it is not good. The model misses 88% of actual motions (low recall), which defeats the purpose of motion detection. The high accuracy is misleading because most frames have no motion, so guessing "no motion" often looks good but is useless.

Key Result

Recall and precision are key for motion detection; accuracy alone can be misleading due to class imbalance.

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