Merge pull request #247203 from mhopkins-msft/mh-avi

Emotions updates

Author: v-ccolin

articles/azure-video-indexer/concepts-overview.md

@@ -2,7 +2,7 @@
 title:  Azure AI Video Indexer terminology & concepts overview
 description: This article gives a brief overview of Azure AI Video Indexer terminology and concepts.
 ms.topic: conceptual
-ms.date: 12/01/2022
+ms.date: 08/02/2023
 ms.author: juliako
 ---
 
@@ -12,13 +12,13 @@ This article gives a brief overview of Azure AI Video Indexer terminology and co
 
 ## Artifact files
 
-If you plan to download artifact files, beware of the following: 
+If you plan to download artifact files, beware of the following warning: 
 
 [!INCLUDE [artifacts](./includes/artifacts.md)]
 
 ## Confidence scores
 
-The confidence score indicates the confidence in an insight. It is a number between 0.0 and 1.0. The higher the score the greater the confidence in the answer. For example: 
+The confidence score indicates the confidence in an insight. It's a number between 0.0 and 1.0. The higher the score the greater the confidence in the answer. For example: 
 
 ```json
 "transcript":[
@@ -45,7 +45,7 @@ Use textual and visual content moderation models to keep your users safe from in
 
 ## Insights	
 
-Insights contain an aggregated view of the data: faces, topics, emotions. Azure AI Video Indexer analyzes the video and audio content by running 30+ AI models, generating rich insights. 
+Insights contain an aggregated view of the data: faces, topics, text-based emotion detection. Azure AI Video Indexer analyzes the video and audio content by running 30+ AI models, generating rich insights. 
 
 For detailed explanation of insights, see [Azure AI Video Indexer insights](insights-overview.md).
 
@@ -55,7 +55,7 @@ Azure AI Video Indexer selects the frame(s) that best represent each shot. Keyfr
 
 ## Time range vs. adjusted time range	
 
-Time range is the time period in the original video. Adjusted time range is the time range relative to the current playlist. Since you can create a playlist from different lines of different videos, you can take a 1-hour video and use just 1 line from it, for example, 10:00-10:15. In that case, you will have a playlist with 1 line, where the time range is 10:00-10:15 but the adjusted time range is 00:00-00:15.	
+Time range is the time period in the original video. Adjusted time range is the time range relative to the current playlist. Since you can create a playlist from different lines of different videos, you can take a one-hour video and use just one line from it, for example, 10:00-10:15. In that case, you'll have a playlist with one line, where the time range is 10:00-10:15 but the adjusted time range is 00:00-00:15.	
 
 ## Widgets
 

articles/azure-video-indexer/detected-clothing.md

@@ -2,32 +2,32 @@
 title: Enable detected clothing feature
 description: Azure AI Video Indexer detects clothing associated with the person wearing it in the video and provides information such as the type of clothing detected and the timestamp of the appearance (start, end). The API returns the detection confidence level.
 ms.topic: how-to
-ms.date: 11/15/2021
+ms.date: 08/02/2023
 ms.author: juliako
 ---
 
 # Enable detected clothing feature (preview)
 
-Azure AI Video Indexer detects clothing associated with the person wearing it in the video and provides information such as the type of clothing detected and the timestamp of the appearance (start, end). The API returns the detection confidence level.
+Azure AI Video Indexer detects clothing associated with the person wearing it in the video and provides information such as the type of clothing detected and the timestamp of the appearance (start, end). The API returns the detection confidence level. The clothing types that are detected are long pants, short pants, long sleeves, short sleeves, and skirt or dress.
 
 Two examples where this feature could be useful:
 
-* Improve efficiency when creating raw data for content creators, like video advertising, news, or sport games (for example, find people wearing a red shirt in a video archive).
-* Post-event analysis—detect and track a person’s movement to better analyze an accident or crime post-event (for example, explosion, bank robbery, incident).
+- Improve efficiency when creating raw data for content creators, like video advertising, news, or sport games (for example, find people wearing a red shirt in a video archive).
+- Post-event analysis—detect and track a person’s movement to better analyze an accident or crime post-event (for example, explosion, bank robbery, incident).
 
-The newly added clothing detection feature is available when indexing your file by choosing the **Advanced option** -> **Advanced video** or **Advanced video + audio** preset (under Video + audio indexing). Standard indexing will not include this new advanced model.
+The newly added clothing detection feature is available when indexing your file by choosing the **Advanced option** -> **Advanced video** or **Advanced video + audio** preset (under Video + audio indexing). Standard indexing won't include this new advanced model.
 
 :::image type="content" source="./media/detected-clothing/index-video.png" alt-text="This screenshot represents an indexing video option":::  
 
 When you choose to see **Insights** of your video on the [Azure AI Video Indexer](https://www.videoindexer.ai/) website, the People's detected clothing could be viewed from the **Observed People** tracing insight. When choosing a thumbnail of a person the detected clothing became available.
 
 :::image type="content" source="./media/detected-clothing/observed-people.png" alt-text="Observed people screenshot":::  
 
-If you are interested to view People's detected clothing in the Timeline of your video on the Azure AI Video Indexer website, go to **View** -> **Show Insights** and select the **All** option or **View** -> **Custom View** and select **Observed People**. 
+If you're interested to view People's detected clothing in the Timeline of your video on the Azure AI Video Indexer website, go to **View** -> **Show Insights** and select the All option, or **View** -> **Custom View** and select **Observed People**. 
 
 :::image type="content" source="./media/detected-clothing/observed-person.png" alt-text="Observed person screenshot":::  
 
-Searching for a specific clothing to return all the observed people wearing it is enabled using the search bar of either the **Insights** or from the **Timeline** of your video on the Azure AI Video Indexer website .
+Searching for a specific clothing to return all the observed people wearing it's enabled using the search bar of either the **Insights** or from the **Timeline** of your video on the Azure AI Video Indexer website.
 
 The following JSON response illustrates what Azure AI Video Indexer returns when tracing observed people having detected clothing associated:
 
@@ -94,18 +94,11 @@ The following JSON response illustrates what Azure AI Video Indexer returns when
 
 ## Limitations and assumptions
 
-It's important to note the limitations of Detected clothing, to avoid or mitigate the effects of false negatives (missed detections).
- 
-* To optimize the detector results, use video footage from static cameras (although a moving camera or mixed scenes will also give results).
-* People are not detected if they appear small (minimum person height is 200 pixels).
-* Maximum frame size is HD
-* People are not detected if they're not standing or walking.
-* Low-quality video (for example, dark lighting conditions) may impact the detection results.
-* The recommended frame rate at least 30 FPS.
-* Recommended video input should contain up to 10 people in a single frame. The feature could work with more people in a single frame, but the detection result retrieves up to 10 people in a frame with the detection highest confidence.
-* People with similar clothes (for example, people wear uniforms, players in sport games) could be detected as the same person with the same ID number.
-* Obstructions – there maybe errors where there are obstructions (scene/self or obstructions by other people).
-* Pose: The tracks may be split due to different poses (back/front)
+As the detected clothing feature uses observed people tracking, the tracking quality is important. For tracking considerations and limitations, see [Considerations and limitations when choosing a use case](observed-matched-people.md#considerations-and-limitations-when-choosing-a-use-case).
+
+- As clothing detection is dependent on the visibility of the person’s body, the accuracy is higher if a person is fully visible.
+- There maybe errors when a person is without clothing.
+- In this scenario or others of poor visibility, results may be given such as long pants and skirt or dress. 
 
 ## Next steps