AI-102 Implement computer vision solutions Practice Question
A retail company uses Azure Computer Vision to analyze in-store camera feeds to count customers. The solution uses the Detect API on individual frames. Recently, the counts have been inaccurate due to overlapping people. Which action should you take to improve accuracy?
⚠ Common exam trap
Test-takers frequently assume the generic Detect API can handle all scenarios out-of-the-box, overlooking the need for domain-specific fine-tuning when dealing with overlapping objects in crowded environments.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train a Custom Vision object detection model with images of crowded scenes.
The Detect API in Azure Computer Vision is a general-purpose object detection model that may not be optimized for crowded scenes with significant overlap. By training a Custom Vision object detection model with images of crowded scenes, you create a specialized model that learns to distinguish partially occluded people, improving counting accuracy. This approach leverages transfer learning to adapt the model to the specific visual patterns of your store environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Train a Custom Vision object detection model with images of crowded scenes.
Why this is correct
Custom model can learn to detect partially occluded people.
- ✗
Use the Analyze API instead of Detect API.
Why it's wrong here
Analyze API does not provide object detection.
- ✗
Increase the confidence threshold in the Detect API call.
Why it's wrong here
May reduce false positives but doesn't address overlap detection.
- ✗
Switch to Azure Video Indexer for people counting.
Why it's wrong here
Video Indexer is for video analysis, not real-time frame processing.
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