AI-102 Question 168
Select 4You are developing a smart surveillance system using Azure AI Vision's Spatial Analysis feature. The system should detect the presence and movement of people in a retail store to monitor customer flow and trigger alerts if there are too many people in a specific area. Which steps should you include in your solution to achieve this functionality?
- A
Enable the 'People Count' capability in the Spatial Analysis configuration.
- B
Configure the 'Crossing Line' event to detect when people cross predefined zones.
- C
Set up a custom machine learning model for detecting objects other than people.
- D
Integrate the Spatial Analysis output with an Azure Event Hub for real-time alerting.
- E
Deploy the Spatial Analysis container to an on-premises device or Azure IoT Edge.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To implement a people detection and movement monitoring solution using Azure AI Vision's Spatial Analysis, you must configure the relevant capabilities like 'People Count' and 'Crossing Line,' deploy the analysis container to a supported environment, and integrate it with Azure services like Event Hub for real-time alerting. There is no need for a custom machine learning model for this scenario as the Spatial Analysis feature is already optimized for detecting people.
- A. Correct.
This is correct. The 'People Count' capability is essential for detecting the number of people in a specific area within the video feed.
- B. Correct.
This is correct. The 'Crossing Line' event can be used to track movement and detect when people enter or exit certain zones.
- C. Incorrect.
This is incorrect. Azure AI Vision's Spatial Analysis is specifically designed for detecting and analyzing people; custom models for other objects are not needed in this scenario.
- D. Correct.
This is correct. Integrating Spatial Analysis with Azure Event Hub enables real-time alerts based on the detected presence and movement of people.
- E. Correct.
This is correct. Spatial Analysis must be deployed to a supported environment, such as an on-premises device or Azure IoT Edge, to process video feeds.