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. Kinesis Video Streams enables you to quickly build computer vision and ML applications through integration with Amazon Rekognition Video and libraries for ML frameworks such as Apache MxNet, TensorFlow, and OpenCV.
To get started, create and configure a Kinesis video stream with a few clicks from the AWS Management Console. You can then install the Kinesis Video Streams SDK on your devices and start streaming video to AWS for analytics, processing, and storage. With Kinesis Video Streams, you pay only for what you use. There are no upfront commitments or minimum fees.
Amazon Kinesis Video Streams announcement at AWS re:Invent 2017
Stream Video from Millions of Edge Devices
Amazon Kinesis Video Streams provides SDKs that you can install on your devices to make it easy to securely stream video to AWS for storage, analytics, machine learning, and other processing. Kinesis Video Streams can ingest data from edge devices, smartphones, and security cameras, and other data sources such as RADARs, LIDARs, drones, satellites, dashcams, and depth-sensors.
Easily Build Vision-Enabled Apps
Amazon Kinesis Video Streams is integrated with Amazon Rekognition Video, making it easy for you to build applications that take advantage of computer vision and video analytics. You can also build custom applications using popular open-source ML frameworks to process and analyze your video streams.
Amazon Kinesis Video Streams enables you to control access to your streams using AWS Identity and Access Management (IAM). It helps you protect your data by automatically encrypting the data at rest using AWS Key Management Service (KMS), and in transit using the industry-standard Transport Layer Security (TLS) protocol.
Durable, Searchable Storage
Amazon Kinesis Video Streams uses Amazon S3 as the underlying data store, which means your data is stored durably and reliably. You can set and control retention periods for data stored in your streams. Kinesis Video Streams enables you to quickly search and retrieve video fragments based on device-generated and service-generated timestamps.
No Infrastructure to Manage
Amazon Kinesis Video Streams manages all the infrastructure for you. You don’t have to worry about configuration, software updates, failures, or scaling infrastructure as the number of streams and consuming applications grows. Kinesis Video Streams handles all the administration and maintenance required to manage your streams, so you can focus your time on building innovative applications.
Build both Real-time and Batch Applications
Amazon Kinesis Video Streams allows you to build real-time applications that use live streams of data. Kinesis Video Streams also stores the data so you can run batch applications.
How it works
With Amazon Kinesis Video Streams, you can easily stream video and audio from camera-equipped home devices such as baby monitors, webcams, and home surveillance systems to AWS. You can then use the streams to build a variety of smart home applications ranging from simple video playback to intelligent lighting, climate control systems, and security solutions.
Many cities have installed large numbers of cameras at traffic lights, parking lots, shopping malls, and just about every public venue, capturing video 24/7. You can use Amazon Kinesis Video Streams to securely and cost-effectively ingest, store, and analyze this massive volume of video data to help solve traffic problems, help prevent crime, dispatch emergency responders, and much more.
You can use Amazon Kinesis Video Streams to collect a variety of time-encoded data such as RADAR and LIDAR signals, temperature profiles, and depth data from industrial equipment. You can then analyze the data using your favorite machine learning framework including Apache MxNet, TensorFlow, and OpenCV for industrial automation use cases like predictive maintenance. For example, you can predict the lifetime of a gasket or valve and schedule part replacement in advance, reducing downtime and defects in a manufacturing line.