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---
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title: People's detected in a video clothing
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description: This topic gives an overview of a person's detected in a video clothing feature.
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ms.topic: conceptual
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ms.date: 11/15/2021
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ms.author: juliako
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---
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# People's detected clothing (preview)
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Video Analyzer for Media 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.
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Two examples where this feature could be useful:
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* 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).
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* 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).
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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.
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:::image type="content" source="./media/detected-clothing/index-video.png" alt-text="This screenshot represents an indexing video option":::
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When you choose to see **Insights** of your video on the [Video Analyzer for Media](https://www.videoindexer.ai/) (former Video Indexer) 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.
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:::image type="content" source="./media/detected-clothing/observed-people.png" alt-text="Observed people screenshot":::
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If you are interested to view People's detected clothing in the Timeline of your video on the Video Analyzer for Media website, go to **View** -> **Show Insights** and select the **All** option or **View** -> **Custom View** and select **Observed People**.
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:::image type="content" source="./media/detected-clothing/observed-person.png" alt-text="Observed person screenshot":::
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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 Video Analyzer for Media website .
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The following JSON response illustrates what Video Analyzer for Media returns when tracing observed people having detected clothing associated:
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```json
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"observedPeople": [
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{
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"id": 1,
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"thumbnailId": "68bab0f2-f084-4c2b-859b-a951ed03c209",
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"clothing": [
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{
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"id": 1,
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"type": "sleeve",
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"properties": {
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"length": "short"
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}
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},
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{
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"id": 2,
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"type": "pants",
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"properties": {
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"length": "long"
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}
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}
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],
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"instances": [
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{
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"adjustedStart": "0:00:05.5055",
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"adjustedEnd": "0:00:09.9766333",
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"start": "0:00:05.5055",
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"end": "0:00:09.9766333"
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}
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]
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},
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{
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"id": 2,
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"thumbnailId": "449bf52d-06bf-43ab-9f6b-e438cde4f217",
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"clothing": [
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{
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"id": 1,
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"type": "sleeve",
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"properties": {
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"length": "short"
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}
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},
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{
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"id": 2,
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"type": "pants",
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"properties": {
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"length": "long"
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}
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}
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],
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"instances": [
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{
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"adjustedStart": "0:00:07.2072",
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"adjustedEnd": "0:00:10.5105",
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"start": "0:00:07.2072",
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"end": "0:00:10.5105"
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}
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]
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},
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]
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```
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## Limitations and assumptions
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It's important to note the limitations of Detected clothing, to avoid or mitigate the effects of false negatives (missed detections).
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* To optimize the detector results, use video footage from static cameras (although a moving camera or mixed scenes will also give results).
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* People are not detected if they appear small (minimum person height is 200 pixels).
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* Maximum frame size is HD
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* People are not detected if they're not standing or walking.
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* Low-quality video (for example, dark lighting conditions) may impact the detection results.
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* The recommended frame rate at least 30 FPS.
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* 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.
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* 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.
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* Occlusions – there maybe errors where there are occlusions (scene/self or occlusions by other people).
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* Pose: The tracks may be split due to different poses (back/front)
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## Next steps
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[Trace observed people in a video](observed-people-tracing.md)
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articles/azure-video-analyzer/video-analyzer-for-media-docs/observed-people-tracing.md

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ms.author: juliako
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---
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# Trace observed people in a video
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# Trace observed people in a video (preview)
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Video Indexer detects observed people in videos and provides information such as the location of the person in the video frame and the exact timestamp (start, end) when a person appears. The API returns the bounding box coordinates (in pixels) for each person instance detected, including detection confidence.
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The newly added **Observed people tracing** 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.
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:::image type="content" source="./media/observed-people-tracing/youtube-trailer.png" alt-text="Observed people tracing screenshot":::
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When you choose to see **Insights** of your video on the [Video Indexer](https://www.videoindexer.ai/account/login) website, the Observed People Tracing will show up on the page with all detected people thumbnails. You can choose a thumbnail of a person and see where the person appears in the video player.

articles/azure-video-analyzer/video-analyzer-for-media-docs/release-notes.md

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For more information go to [create a Video Analyzer for Media account](https://techcommunity.microsoft.com/t5/azure-ai/azure-video-analyzer-for-media-is-now-available-as-an-azure/ba-p/2912422).
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### People’s clothing detection
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When indexing a video through the advanced video settings, you can view the new **People’s clothing detection** capability. If there are people detected in your media file, you can now view the clothing type they are wearing through the media player.
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## October 2021
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### Embed widgets in your app using Azure Video Analyzer for Media package

articles/azure-video-analyzer/video-analyzer-for-media-docs/toc.yml

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href: language-identification-model.md
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- name: Live stream analysis
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href: live-stream-analysis.md
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- name: Observed people
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href: observed-people-tracing.md
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- name: Observed in a video people
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href: observed-people-tracing.md
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- name: People's detected clothing
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href: detected-clothing.md
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- name: Audio effects detection
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href: audio-effects-detection.md
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- name: Customizing content models

articles/azure-video-analyzer/video-analyzer-for-media-docs/video-indexer-overview.md

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* **Rolling credits**: Identifies the beginning and end of the rolling credits in the end of TV shows and movies.
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* **Animated characters detection** (preview): Detection, grouping, and recognition of characters in animated content via integration with [Cognitive Services custom vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/). For more information, see [Animated character detection](animated-characters-recognition.md).
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* **Editorial shot type detection**: Tagging shots based on their type (like wide shot, medium shot, close up, extreme close up, two shot, multiple people, outdoor and indoor, and so on). For more information, see [Editorial shot type detection](scenes-shots-keyframes.md#editorial-shot-type-detection).
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* **Observed People Tracking**: detects observed people in videos and provides information such as the location of the person in the video frame (using bounding boxes) and the exact timestamp (start, end) and confidence when a person appears. For more information, see [Trace observed people in a video](observed-people-tracing.md).
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* **Observed People Tracking** (preview): detects observed people in videos and provides information such as the location of the person in the video frame (using bounding boxes) and the exact timestamp (start, end) and confidence when a person appears. For more information, see [Trace observed people in a video](observed-people-tracing.md).
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* **People's detected clothing**: detects the clothing types of people appearing in the video and provides information such as long or short sleeves, long or short pants and skirt or dress. The detected clothing are associated with the people wearing it and the exact timestamp (start,end) along with a confidence level for the detection are provided.
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### Audio insights
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---
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title: Azure Industrial IoT platform versions
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description: This article provides an overview of the existing version of the Industrial IoT platform and their support.
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author: jehona-m
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ms.author: jemorina
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author: monikavar
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ms.author: movarshn
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ms.service: industrial-iot
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ms.topic: overview
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ms.date: 03/08/2021
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ms.date: 11/10/2021
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---
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# Azure Industrial IoT Platform v2.8.0 LTS
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# Azure Industrial IoT Platform Release 2.8.1
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We are pleased to announce the release of version 2.8.1 of our Industrial IoT Platform components. This is the first patch update of the 2.8 Long-Term Support (LTS) release. It contains important security updates, bug fixes, and performance optimizations.
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We are pleased to announce the declaration of Long-Term Support (LTS) for version 2.8.0. While we continue to develop and release updates to our ongoing projects on GitHub, we now also offer a branch that will only get critical bug fixes and security updates starting in July 2021. Customers can rely upon a longer-term support lifecycle for these LTS builds, providing stability and assurance for the planning on longer time horizons our customers require. The LTS branch offers customers a guarantee that they will benefit from any necessary security or critical bug fixes with minimal impact to their deployments and module interactions. At the same time, customers can access the latest updates in the [main branch](https://github.com/Azure/Industrial-IoT) to keep pace with the latest developments and fastest cycle time for product updates.
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## Azure Industrial IoT Platform Release 2.8
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We are pleased to announce the declaration of Long-Term Support (LTS) for version 2.8. While we continue to develop and release updates to our ongoing projects on GitHub, we now also offer a branch that will only get critical bug fixes and security updates starting in July 2021. Customers can rely upon a longer-term support lifecycle for these LTS builds, providing stability and assurance for the planning on longer time horizons our customers require. The LTS branch offers customers a guarantee that they will benefit from any necessary security or critical bug fixes with minimal impact to their deployments and module interactions. At the same time, customers can access the latest updates in the [main branch](https://github.com/Azure/Industrial-IoT) to keep pace with the latest developments and fastest cycle time for product updates.
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## Version history
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|Version |Type |Date |Highlights |
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|-------------|-----------------------|-------------|---------------------------------------|
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|2.5.4 |Stable |March 2020 |IoT Hub Direct Method Interface, control from cloud without any additional microservices (standalone mode), OPC UA Server Interface, uses OPC Foundation's OPC stack - [Release notes](https://github.com/Azure/Industrial-IoT/releases/tag/2.5.4)|
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|[2.7.206](https://github.com/Azure/Industrial-IoT/tree/release/2.7.206) |Stable |January 2021 |Configuration through REST API (orchestrated mode), supports Samples telemetry format as well as PubSub - [Release notes](https://github.com/Azure/Industrial-IoT/releases/tag/2.7.206)|
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|[2.8.0](https://github.com/Azure/Industrial-IoT/tree/release/2.8) |Long-term support (LTS)|July 2021 |IoT Edge update to 1.1 LTS, OPC stack logging and tracing for better OPC Publisher diagnostics, Security fixes|
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|[2.8](https://github.com/Azure/Industrial-IoT/tree/2.8.0) |Long-term support (LTS)|July 2021 |IoT Edge update to 1.1 LTS, OPC stack logging and tracing for better OPC Publisher diagnostics, Security fixes - [Release notes](https://github.com/Azure/Industrial-IoT/releases/tag/2.8.0)|
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|[2.8.1](https://github.com/Azure/Industrial-IoT/tree/2.8.1) |Patch release for LTS 2.8|November 2021 |Critical bug fixes, security updates, performance optimizations for LTS v2.8|
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## Next steps
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> [!div class="nextstepaction"]
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> [What is Industrial IoT?](overview-what-is-industrial-iot.md)
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> [What is Industrial IoT?](overview-what-is-industrial-iot.md)

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