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AWS-Integrated Rover Surveillance with Real-Time Video Analytics and IoT Control

Written by - Sadhna S

December 31, 2025
6 min read
AI MLAWS Cloud Computing Cloud Architecture Video Analytics Real-Time Video Processing Artificial Intelligence Machine Learning Amazon Rekognition Kinesis Video Streams Internet of Things (IoT) AWS IoT Core Smart Surveillance Serverless Architecture

Introduction

Surveillance systems are rapidly evolving from static camera setups to intelligent, autonomous platforms capable of real-time analysis and automated response. By combining cloud computing, AI-driven video analytics, and IoT, organizations can now deploy smart rover-based surveillance systems that operate with minimal human intervention.

This blog explains how to design an AWS-integrated rover surveillance architecture that enables real-time video streaming, intelligent event detection, and IoT-based device control.

System Overview

The solution uses a camera-mounted rover that continuously captures live video and streams it to AWS. The video is processed, analyzed using AI models, and converted into actionable insights that can trigger physical actions such as alarms, lights, or alerts.

This architecture is highly scalable, serverless, and suitable for enterprise-grade deployments.

Architecture Workflow

1. Camera Mounted on Rover

A high-resolution camera installed on the rover captures live video while the rover patrols a defined area.

2. Real-Time Video Streaming

The live feed is streamed to Amazon Kinesis Video Streams, enabling low-latency ingestion and reliable real-time processing.

3. Secure Video Storage

Video fragments are stored in Amazon S3, providing durability, security, and event-driven triggers for downstream processing.

4. Workflow Orchestration

AWS Step Functions orchestrate the entire pipeline by invoking:

  • AWS Lambda for business logic
  • AWS Elemental MediaConvert for video optimization if required

5. Metadata and State Management

Processed data and detection metadata are stored in Amazon DynamoDB, enabling fast access and real-time updates.

6. Web and API Layer

  • AWS AppSync provides real-time GraphQL APIs
  • AWS Amplify hosts the frontend application
  • Amazon API Gateway + AWS Lambda expose secure REST APIs

Users can monitor live feeds and analytics from a web browser.

7. AI-Powered Video Analysis

Amazon Rekognition Custom Labels analyzes video frames to detect:

  • Intrusions
  • Movement
  • Custom-defined objects or events

This enables intelligent decision-making without manual monitoring.

8. IoT-Based Device Control

Based on AI outcomes, AWS IoT Core triggers physical devices such as:

  • Warning lights
  • Sirens
  • Automated responses

This closes the loop between detection and action.

Key Benefits

  • Real-time video processing with low latency
  • Fully serverless and scalable architecture
  • AI-driven detection and insights
  • Secure cloud-to-device communication
  • Modular and enterprise-ready design

Use Cases

  • Industrial and warehouse security
  • Smart perimeter monitoring
  • Autonomous patrol systems
  • Smart agriculture surveillance
  • Remote infrastructure inspection

Conclusion

By integrating AWS video services, AI analytics, and IoT control, this rover surveillance system delivers a powerful and future-ready solution. The architecture ensures scalability, reliability, and intelligent automation—making it ideal for modern surveillance and monitoring needs.

1. Video Capture on Rover

A high-resolution camera mounted on the rover continuously captures live video while moving through the target area.

2. Real-Time Video Streaming

The video feed is streamed to Amazon Kinesis Video Streams, enabling low-latency, real-time ingestion of video data into AWS.

Why Kinesis?

  • Designed for live video
  • Scales automatically
  • Supports real-time processing

3. Secure Video Storage

Video chunks are stored in Amazon S3, providing:

  • Durable storage
  • Event-driven processing
  • Easy integration with analytics services

4. Workflow Orchestration

AWS Step Functions coordinate the processing workflow, invoking:

  • AWS Lambda for logic execution
  • AWS Elemental MediaConvert for video format optimization if required

This ensures a clean, serverless orchestration layer.

5. Metadata & State Management

Processed insights and metadata are stored in Amazon DynamoDB, enabling:

  • Fast queries
  • Real-time dashboard updates
  • Event tracking

6. Web & API Layer

  • AWS AppSync (GraphQL) provides real-time data sync
  • AWS Amplify hosts the frontend
  • Amazon API Gateway + Lambda expose secure APIs

Users can monitor live feeds and system status via a browser interface.

7. AI-Powered Video Analysis

Amazon Rekognition Custom Labels analyzes video frames to:

  • Detect objects
  • Identify anomalies
  • Recognize custom scenarios (intrusion, motion, hazards)

This enables intelligent decision-making without human intervention.

8. IoT-Based Control

Based on AI results, AWS IoT Core triggers physical actions such as:

  • Turning on a warning light
  • Activating alarms
  • Sending signals to connected devices

This closes the loop between detection and action.