OpenPose overview in Computer Vision - Model Metrics & Evaluation

Metrics & Evaluation - OpenPose overview

Which metric matters for OpenPose and WHY

OpenPose detects body keypoints like joints in images or videos. The main metric to check how well it works is Percentage of Correct Keypoints (PCK). PCK measures how many predicted points are close enough to the true points. This matters because OpenPose needs to find exact body parts to be useful.

Confusion matrix or equivalent visualization

    True Keypoints:    O O O O O
    Predicted Points:  O O X O O

    Here, 'O' means correct keypoint detected,
    'X' means missed or wrong keypoint.

    Count of correct points (TP): 4
    Count of missed points (FN): 1
    False positives (FP) are rare since OpenPose predicts fixed points.
    

Precision vs Recall tradeoff with examples

In OpenPose, precision means how many detected points are actually correct. Recall means how many true points were found.

If precision is high but recall is low, OpenPose finds few points but they are mostly right. This is safe but misses body parts.

If recall is high but precision is low, OpenPose finds many points but some are wrong. This can confuse applications.

For example, in sports analysis, high recall is important to track all movements. In medical use, high precision is needed to avoid wrong body part detection.

What "good" vs "bad" metric values look like for OpenPose

Good: PCK above 85% means most keypoints are detected accurately. Precision and recall both above 80% show balanced and reliable detection.

Bad: PCK below 60% means many keypoints are missed or wrong. Precision or recall below 50% means the model is unreliable for real use.

Common pitfalls in OpenPose metrics

• Ignoring scale: Keypoint distance thresholds must adjust for image size, or PCK will be misleading.
• Overfitting: Model may work well on training poses but fail on new people or angles.
• Data leakage: Testing on images similar to training can inflate metrics falsely.
• Ignoring occlusions: Missing points due to blocked body parts can lower recall unfairly.

Self-check question

Your OpenPose model has 90% precision but only 40% recall. Is it good for tracking full body movement? Why or why not?

Answer: No, because it misses many true keypoints (low recall). It finds few points but mostly correct. For full body tracking, missing many points is a problem.

Key Result

Percentage of Correct Keypoints (PCK) is key to measure OpenPose accuracy in detecting body parts.

Practice

1. What is the main purpose of OpenPose in computer vision?

easy

A. To classify objects like cars and animals

B. To detect human body keypoints and poses in images or videos

C. To enhance image resolution

D. To generate 3D models from 2D images

Solution

1. Step 1: Understand OpenPose's function

   OpenPose is designed to find human body parts and poses in images or videos.

2. Step 2: Compare with other options

   Options B, C, and D describe different tasks unrelated to pose detection.

3. Final Answer:

   To detect human body keypoints and poses in images or videos -> Option B

4. Quick Check:

   OpenPose = Human pose detection [OK]

Hint: OpenPose = human pose keypoints detection [OK]

Common Mistakes:

• Confusing OpenPose with object classification
• Thinking OpenPose enhances image quality
• Assuming OpenPose creates 3D models

2. Which of the following is the correct step to use OpenPose in a program?

easy

A. Use OpenPose to classify image colors

B. Directly print the image without loading any model

C. Load the OpenPose model, process the image, then extract keypoints

D. Skip model loading and only display raw pixels

Solution

1. Step 1: Recall OpenPose usage steps

   OpenPose requires loading its model, processing images, and extracting keypoints.

2. Step 2: Eliminate incorrect options

   Options B, C, and D ignore model loading or misuse OpenPose for unrelated tasks.

3. Final Answer:

   Load the OpenPose model, process the image, then extract keypoints -> Option C

4. Quick Check:

   Model load + process + keypoints = correct usage [OK]

Hint: Always load model before processing images [OK]

Common Mistakes:

• Skipping model loading step
• Using OpenPose for color classification
• Trying to process images without model

3. Given this Python snippet using OpenPose:

keypoints = openpose.process(image)
print(len(keypoints))

What does len(keypoints) represent?

medium

A. The number of detected people in the image

B. The number of pixels in the image

C. The number of colors detected

D. The number of image channels

Solution

1. Step 1: Understand what keypoints hold

   OpenPose returns keypoints for each detected person; length equals number of people detected.

2. Step 2: Compare with other options

   Pixels, colors, and channels are unrelated to keypoints length.

3. Final Answer:

   The number of detected people in the image -> Option A

4. Quick Check:

   len(keypoints) = people count [OK]

Hint: Keypoints list length = people detected [OK]

Common Mistakes:

• Thinking length is pixel count
• Confusing keypoints with colors
• Assuming length is image channels

4. You run this code snippet but get an error:

keypoints = openpose.process(image)
print(keypoints.shape)

What is the likely cause?

medium

A. keypoints is a list, not a numpy array, so it has no shape attribute

B. The image variable is not defined

C. OpenPose model was not loaded

D. print() function is used incorrectly

Solution

1. Step 1: Identify error cause

   keypoints from OpenPose is usually a list, which does not have a .shape attribute.

2. Step 2: Check other options

   Image undefined or model not loaded would cause different errors; print() usage is correct.

3. Final Answer:

   keypoints is a list, not a numpy array, so it has no shape attribute -> Option A

4. Quick Check:

   List has no .shape attribute [OK]

Hint: Use len() for lists, not .shape [OK]

Common Mistakes:

• Assuming keypoints is a numpy array
• Ignoring variable definition errors
• Blaming print() function

5. You want to use OpenPose to analyze a video with multiple people moving. Which approach is best to get accurate pose tracking over time?

hard

A. Process only the first frame and reuse keypoints for all frames

B. Process frames randomly without linking keypoints

C. Skip OpenPose and use color detection instead

D. Process each video frame with OpenPose and link detected keypoints across frames

Solution

1. Step 1: Understand video pose tracking

   Accurate tracking requires processing each frame and linking poses over time.

2. Step 2: Evaluate other options

   Reusing first frame keypoints ignores movement; color detection is unrelated; random processing loses continuity.

3. Final Answer:

   Process each video frame with OpenPose and link detected keypoints across frames -> Option D

4. Quick Check:

   Frame-by-frame + linking = accurate tracking [OK]

Hint: Track poses frame-by-frame, link keypoints [OK]

Common Mistakes:

• Using only first frame keypoints
• Confusing color detection with pose tracking
• Ignoring temporal linking of poses