AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail store uses ceiling-mounted cameras to analyze customer traffic flow. They need to detect when a person enters a specific aisle and determine the direction they are walking. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
A common mix-up: candidates confuse general image analysis or facial recognition with the specialized spatial tracking capability, not realizing that Spatial Analysis is the only Azure service designed for real-time people counting and direction detection in physical spaces.
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
✓
People counting (Spatial Analysis)
Spatial Analysis, part of Azure Computer Vision, uses ceiling-mounted cameras to track people's movement and direction in a physical space. It specifically provides people counting and trajectory analysis, making it ideal for detecting when a person enters an aisle and determining their walking direction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image Analysis dense captioning
Why it's wrong here
Dense captioning is an image analysis capability that generates descriptive sentences for multiple regions within a single image, identifying objects, actions, and spatial relationships in localized areas. However, it is designed for static scene understanding and produces language descriptions rather than tracking individuals over time. It cannot follow a person's trajectory across video frames or determine movement direction, making it unsuitable for analyzing customer flow from ceiling-mounted cameras.
- ✗
Facial recognition
Why it's wrong here
Facial recognition technology identifies or verifies a person's identity by analyzing facial features and matching them against a known database, such as customer profiles or watchlists. While it can detect and locate faces, it does not extract body posture, movement vectors, or trajectory information from a video feed. For a retail store analyzing customer movement and direction, facial recognition provides no insight into how people walk, congregate, or navigate the store; it only answers 'who is this?' not 'where are they going?'
- ✓
People counting (Spatial Analysis)
Why this is correct
People counting via Spatial Analysis is an Azure Computer Vision capability specifically designed to detect and track individuals in a video stream, providing counts of people entering, exiting, or dwelling in a given zone. It uses person detection and cross-frame tracking algorithms to follow the same individual across consecutive frames, enabling calculation of movement direction and flow patterns. This directly matches the retail store's need to analyze customer movement using ceiling-mounted cameras, offering actionable metrics like queue length, wait time, and foot-traffic routes without identifying individuals.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) extracts printed or handwritten text from images and converts it into machine-readable strings, focusing solely on textual content. It has no object detection, person tracking, or motion analysis capabilities, so it cannot perceive humans, their locations, or their movement. In a retail camera feed, OCR would only process visible text like signs or receipts, completely ignoring the people standing beneath the ceiling-mounted camera, making it useless for customer movement analytics.
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