AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail company wants to automatically analyze in-store video footage to count the number of customers entering and exiting through different doors. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Test-takers frequently confuse face detection with person detection, assuming that counting people requires detecting faces, but face detection fails when faces are not visible, whereas object detection with the 'person' class works on full bodies regardless of orientation.
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
✓
Object detection
Object detection is the correct capability because it can identify and locate multiple instances of people within a video frame, drawing bounding boxes around each person. This allows the system to track individuals across frames and count them as they cross virtual lines at doorways, distinguishing between entering and exiting movements. Optical Character Recognition (OCR), image classification, and face detection lack the spatial localization and multi-instance tracking required for this specific counting task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) is designed to extract machine-printed or handwritten text from images and convert it into machine-readable strings. It does not have any capability to detect, localize, or count people, because it operates on character shapes and linguistic patterns rather than body or face features. Therefore, OCR cannot satisfy the requirement to automatically analyze in-store customer presence.
- ✗
Image classification
Why it's wrong here
Image classification assigns a single class label (for example, 'crowded' or 'empty') to the entire image based on global visual features. It does not produce spatial information such as bounding boxes, so it cannot count individual people or distinguish one person from another. Even if it predicts a crowding level, it lacks the granularity needed for automatic people counting or tracking.
- ✓
Object detection
Why this is correct
Object detection is the correct approach because it both identifies the class of each object (e.g., 'person') and outputs a bounding box around each occurrence. This localization allows a system to count the number of people in a frame, and by tracking bounding boxes across frames, to analyze movement patterns and in-store traffic. It is purpose-built for multi-object scenarios where the exact positions and counts are required.
- ✗
Face detection
Why it's wrong here
Face detection is a specialized form of object detection that targets human faces, not whole bodies. In a retail setting, many customers may be turned away from the camera, wearing masks, or partially occluded by shelves, so faces are not always visible, leading to undercounting. Additionally, face detection does not provide body-level spatial analysis, making it unreliable for general person counting and movement tracking.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Image classification
Image classification is the process of teaching a computer to look at a picture and decide what category or label best describes the main object or scene in that picture.
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
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