You can help by adding to it.
Now imagine accomplishing all of this with low latency and without a single server to manage In this post, I present a serverless solution that uses Amazon Rekognition and other AWS services for low-latency video frame analysis.
The solution is a prototype that captures a live video, analyzes it contents, and sends an alert when it detects a certain object. I then give you the tools that you need to configure, build, and run the prototype.
Finally, I show you the prototype in action. Our use case The prototype addresses a specific use case: At a high level, it works like this: A camera surveils a particular area, streaming video over the network to a video capture client.
The client samples video frames and sends them to AWS services, where they are analyzed and stored with metadata. After you receive an SMS alert, you will likely want to know what caused it. For that, the prototype displays sampled video frames with low latency in a web-based user interface.
How you define low latency depends on the nature of the application. Low latency can range from microseconds to a few seconds. If you use a camera for surveillance, as in our prototype, the time between the capture of unusual activity and the triggering of an alarm can be a few seconds and still be considered a low-latency response.
In the following architecture diagram, an arrow represents a step done by an element in the architecture. An arrow starts at the element initiating the step. It ends at an element used in the step. Video capture client samples a frame off of a live video stream It all starts with a digital camera shooting a live video stream.
You can use an IP camera or, for demo purposes, your smartphone. If you use a smartphone, you can shoot and stream video using an IP camera app. You can modify the source code of the video capture client to capture frames from an H. Because of its lower bandwidth requirements, H.
If you plan on sampling frames off of an H. The script accepts two parameters: The frame sampling rate is the rate at which video frames are captured and sent for subsequent processing.
The default rate is one frame every 30 frames. Video capture client packages and streams video frames When the video capture client samples a particular frame, it packages the JPEG-formatted bytes of that frame into an object. It also adds a number of useful attributes to the object, such as the approximate time that the frame was captured.
Image Processor polls the Amazon Kinesis stream, fetching one or more packaged video frames. To learn more about how Lambda and Amazon Kinesis integrate, see the documentation.
Image Processor basedecodes the package, then deserializes it, thereby creating a Python object. Amazon Rekognition makes it easy to add image analysis to your applications.
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