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Endpoint to Cloud Datapipeline

Our team successfully designed and deployed a robust AWS-based infrastructure for real-time audio-video capture, streaming, and processing. The solution integrates Linux application development to handle multi-source data ingestion, cloud streaming, and ML-based processing.

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Our role:​

  • AWS Infrastructure Setup​​

    • Amazon Kinesis Video Streams (KVS): Real-time ingestion and storage of video and audio streams.

    • AWS Data Firehose: Continuous streaming of sensor data to AWS storage and analytics services.

    • AWS Lambda: Automated processing of video and audio streams.

    • Amazon S3: Storage for archived media and processed data.

    • AWS IoT Core: Securely ingesting data from various sensors.

    • Amazon SageMaker: Hosting and running ML models for processing and analysis.​

  • Linux Application Development

    • Audio Capture from I2S and USB Microphones. Integrated ALSA (Advanced Linux Sound Architecture) for low-latency audio capture.

    • Used GStreamer to process and encode audio streams.

    • Supported multiple sample rates and formats (PCM, AAC, Opus).

  • Video Capture from Multiple Cameras

    • Supported MIPI CSI and USB cameras for high-resolution video capture.

    • Utilized OpenCV and GStreamer for real-time frame capture and processing.

    • Implemented multi-threaded handling to support simultaneous camera streams.

  • Streaming Audio and Video to AWS Cloud via KVS

    • Integrated AWS SDK for Kinesis Video Streams (KVS).

    • Employed FFmpeg and GStreamer for real-time encoding (H.264 for video, AAC for audio).

    • Optimized network bandwidth using adaptive bitrate streaming.​

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  • Parsing Audio and Video Data from KVS

    • Developed a serverless AWS Lambda function to extract and process metadata.

    • Implemented Amazon Rekognition and Transcribe for automated content analysis.

    • Stored parsed data in DynamoDB for further indexing.

  • Decoding Audio and Video for ML Model Processing

    • Deployed TensorFlow and PyTorch models for real-time inference.

    • Applied pre-processing filters to enhance video and audio clarity.

    • Integrated results with AWS IoT for real-time notifications and analytics.

  • Sensor Data Capture and Streaming to AWS Cloud using Data Firehose

    • Collected data from temperature, motion, and environmental sensors.

    • Used MQTT and AWS IoT Core for secure data transmission.

    • Configured AWS Data Firehose to stream sensor logs to Amazon S3 and Redshift.

ATRI Solutions is a trusted technology partner for product ideation, development and deployment.

Austin, TX

Boulder, CO

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 ATRI Solutions

Singapore

Ahmedabad, IN

Pune, IN

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