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Amazon Kinesis Video Streams

Capture, process, and store video streams for analytics and machine learning
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What is Amazon Kinesis Video Streams?

Amazon Kinesis Video Streams makes it easy to securely stream video from connected devices to AWS for analytics, machine learning (ML), and other processing. Kinesis Video Streams automatically provisions and elastically scales all the infrastructure needed to ingest streaming video data from millions of devices. It also durably stores, encrypts, and indexes video data in your streams, and allows you to access your data through easy-to-use APIs.
Amazon Kinesis Video Streams is a tool in the Video Streaming category of a tech stack.

Who uses Amazon Kinesis Video Streams?


16 developers on StackShare have stated that they use Amazon Kinesis Video Streams.

Amazon Kinesis Video Streams Integrations

Amazon EC2 Container Service, TensorFlow, OpenCV, AWS IAM, and Amazon Kinesis are some of the popular tools that integrate with Amazon Kinesis Video Streams. Here's a list of all 8 tools that integrate with Amazon Kinesis Video Streams.
Decisions about Amazon Kinesis Video Streams

Here are some stack decisions, common use cases and reviews by companies and developers who chose Amazon Kinesis Video Streams in their tech stack.

Harsh Solanki
Machine Learning Engineer at Quantiphi · | 4 upvotes · 23.4K views

We have to process the video stream to identify emotions. For which we need to use Amazon Rekognition/custom model on Amazon SageMaker. With Kinesis WebRTC Javascript SDK, currently, video can be streamed only into the kinesis signaling channel. Signaling channel data is available for streaming only and not processing (ML). So, how can we get real-time data for processing into Kinesis Streams from the frontend?

For streaming the video from frontend to backend into the Amazon Kinesis Video Streams for processing, we tested with Kinesis webRTC JavaScript SDK, and we are facing issues while implementing as mentioned above, so would Chime SDK serve as an alternative to this?

In Rekognition, "create-stream-processor" has a settings parameter. This currently only supports FaceSearch. We are looking to Detect and analyze faces. Is that possible with "create-stream-processor" in the Python SDK? Or do we have to use the Java SDK?

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We would like to connect a number of (about 25) video streams, from an Amazon S3 bucket containing video data to endpoints accessible to a Docker image, which, when run, will process the input video streams and emit some JSON statistics.

The 25 video streams should be synchronized. Could people share their experiences with a similar scenario and perhaps offer advice about which is better (Wowza, Amazon Kinesis Video Streams) for this kind of problem, or why they chose one technology over the other?

The video stream duration will be quite long (about 8 hours each x 25 camera sources). The 25 video streams will have no audio component. If you worked with a similar problem, what was your experience with scaling, latency, resource requirements, config, etc.?

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Amazon Kinesis Video Streams's Features

  • Device SDKs to securely stream data from devices
  • Durable storage
  • APIs for real-time and batch data retrieval
  • Video stream parser library
  • Video playback in the console
  • Build-in integration with Amazon Rekognition Video
  • Automatic data encryption in transit
  • Automatic data encryption at rest with AWS KMS
  • Access control with AWS IAM
  • Automatic indexing for search and retrieval
  • Fully managed
  • Pay per use

Amazon Kinesis Video Streams Alternatives & Comparisons

What are some alternatives to Amazon Kinesis Video Streams?
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Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.
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jQuery is a cross-platform JavaScript library designed to simplify the client-side scripting of HTML.
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Amazon Kinesis Video Streams's Followers
51 developers follow Amazon Kinesis Video Streams to keep up with related blogs and decisions.