Scene Identification using Amazon Rekognition with Raspberry Pi

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Scene Identification using Amazon Rekognition with Raspberry Pi

SKU: Scene Identification using Amazon Rekognition with Raspberry Pi Category:

Description

ABSTRACT

Amazon Web Service is one of the top cloud services, where we can use some services like Machine learning, IoT, SQL etc. In among the Machine learning services we are using Rekognition service to classify the object which presents in the image. For example: If we capture the image of the traffic area, it will result the data as JSON format, which the data contains the Traffic signals, pedestrians, cars, bikes etc. For such kind of applications, we are using Amazon services.

BLOCK DIAGRAM

Block diagram of     Scene Identification using Amazon Rekognition with Raspberry Pi

BLOCK DIAGRAM DESCRIPTION

In the above block diagram, Camera is connected to the Raspberry Pi,  to capture the scenario, which we have to identify the things present there.The monitor is connected to the Raspberry Pi to view the result.

 

PROJECT DESCRIPTION

Raspberry Pi system will connect to the Amazon cloud, by having the right authentication with secret keys. Then by passing images to the Amazon cloud, we will get the result by detecting the objects or scenes which present in the Picture. This can be done by interfacing camera with the system, which will capture the image when we press the key, then it supposed to analyze the image in cloud and result as JSON format. By splitting the dictionary to get different scenes present in that image.

 

HARDWARE REQUIRED

  • Raspberry Pi
  • Power Adapter
  • HDMI to VGA converter (optional, when connecting to Monitor)
  • Amazon

SOFTWARE REQUIRED

  • SD Card Formatter
  • Win32 Disk Imager (or) Etcher

 

LIBRARIES USED

  • Time library (For Delay functions)

 

CONCLUSION

Amazon Rekognition is used for many Machine learning applications on cloud, this scene, and object identification can be used in an autonomous system to know the object present in the path.

Additional information

Weight0.000000 kg

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