Cloud Video Processing: Street Signals Detection and Recognition

  • Architecture illustrations and analysis
  • Brief description of an image classifier
  • Code snippet
  • V2X technologies
  • Soft-real time validation and local testing of a serverless application
  • Communication privacy solutions
  • Cloud resources allocation
  1. data storing
  2. data computing
  3. data communication
Cloud Services Architecture
  1. the first car does the street profile acquisition (through an on board cam for example) and send it to a S3 bucket formerly created, input_bucket (“first car” means a V2X application that we can simply simulate as a script that capture video from a PC webcam).
  2. The video loading on S3 triggers a lambda function, the principal actor of this architecture, that represents the classifier whose aim is to compute the video, analyze it and give the response in the form of the same video but with the signal detection on it.
  3. After the analysis, the lambda loads the processed video into a different S3 bucket, output_bucket.
  4. In order to notify the passive clients waiting for this kind of information, the lambda function rely on IoT Core. This service is based on MQTT protocol who follows publish-subscribe paradigm. Thus after configured “send topics” and “listening topics”, lambda can notify through these the analysis completion.
  5. The message arrives to passive client in a specific form knows a priori (DENM) containing informations like: what is the API for output_bucket, where’s the location of the signal (where the active client got the street profile acquisition) and what is the name of the processed file and so on.
  6. Thanks to these informations the passive client (V2X application) can download from S3 the processed video and, for example, show it on board to the drivers or show the signal position on navigator.
  • object detection: HCC detects all the object it has been trained for (“Men at Work” signals).
  • object recognition: SURF algorithm executes a feature extraction from the detected object and from the reference image. Then a feature matching through BF matcher is performed. If the match overcomes a threshold, the frame contains the signal of interest who will be marked with a blue rectangle.



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Monteiro Del Prete

Monteiro Del Prete

Master’s Degree student in Artificial Intelligence 🧠