Aftom-Ai is an intelligent surveillance automation platform designed to detect violations captured through CCTV cameras using machine learning and real-time data streaming.

Client

Aftom-AI

Industry

Streaming

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casestudy heading Icon Project Overview

Aftom-Ai is an intelligent surveillance automation platform designed to detect violations captured through CCTV cameras using machine learning and real-time data streaming. The system identifies abnormal or rule-breaking activities (such as unauthorized access, restricted zone entry, safety violations, etc.) and instantly notifies the respective CCTV system owner.
The solution leverages machine learning for object and behavior detection, while real-time communication is facilitated through Apache Kafka and WebSockets. This enables immediate event alerts and live monitoring with near-zero latency.

casestudy heading Icon Objective & Project Scope

  • Automate surveillance monitoring to reduce manual intervention.
  • Detect and classify violations in real-time using ML models.
  • Send instant alerts to CCTV camera owners for quick response.
  • Provide a scalable microservice architecture to handle multiple camera feeds efficiently.
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Platform Experience Design

User flow analysis for surveillance monitoring and alert workflows Journey mapping for real-time violation detection and incident response Wireframing and interactive prototypes for live feeds and control dashboards High-fidelity UI design aligned with intelligent security and monitoring needs

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Real-Time Platform Development

Responsive front-end development for monitoring and management panels Integration of real-time data streaming using Apache Kafka and WebSockets Dashboard development for alerts, events, and system insights Optimized architecture for high performance and near-zero latency updates

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System Testing & Optimization

Cross-browser and cross-device testing for monitoring interfaces Functional testing of detection alerts and real-time event delivery Performance optimization for live streaming and data processing Usability improvements to support faster decision-making and response

casestudy heading Icon Roles & Responsibilities

Roles
  • Designed user flows and dashboards for real-time surveillance and alert management
  • Developed responsive frontend interfaces for live monitoring and violation tracking
  • Integrated machine learning models for object and behavior detection
  • Implemented real-time data streaming using Apache Kafka and WebSockets
  • Optimized system performance for low-latency alerts and continuous monitoring
  • Conducted testing to ensure reliability, scalability, and system stability

casestudy heading Icon Key Features

Real-Time Violation Detection

Automatically detects unauthorized access, restricted zone entry, and safety violations using machine learning models.

Live CCTV Monitoring

Provides real-time video feeds with instant insights for continuous surveillance and faster response.

Instant Event Notifications

Delivers immediate alerts to system owners through low-latency streaming and real-time communication.

ML-Powered Object & Behavior Analysis

Identifies objects and abnormal behavior patterns to ensure accurate and intelligent monitoring.

casestudy heading Icon Project Challenges

The project required addressing real-time detection accuracy, low-latency data processing, and system scalability while ensuring reliable and continuous surveillance operations across multiple CCTV streams.

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Real-Time Detection Accuracy

Implemented optimized machine learning models trained for object and behavior detection to improve accuracy and reliability.

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Low-Latency Event Processing

Integrated Apache Kafka and WebSockets to enable near-zero latency data streaming and real-time communication.

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High-Volume Data Handling

Designed a scalable streaming architecture capable of handling high data throughput efficiently.

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System Reliability & Monitoring

Implemented robust error handling, monitoring, and performance optimization for stable operations.

casestudy heading Icon Project Approaches

Our team built effective solution for Aftom AI

Violation Detection Using ML Models: Machine learning models analyze live CCTV feeds. Detects behavior and event patterns indicating violations. Supports multiple camera inputs simultaneously.

Real-Time Notification System: Uses Apache Kafka for streaming event data between services. WebSockets enable live push notifications to users. Alerts include violation type, snapshot, timestamp, and camera ID.

Owner Dashboard: Live camera feed preview and event timeline. View logs of past violations with searchable and filterable UI. Real-time alerts displayed instantly without page refresh.

Scalable Microservice Architecture: Each processing stage (detection, analytics, notification) runs as an independent service. Deployment-ready with Docker and compatible with AWS cloud infrastructure.

Solution Aviox Built for Aftom AI

This approach focused on keeping things simple, reliable, and ready to grow with Aftom AI needs.

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Technologies

Tech Stack

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React JS

Front End

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Python, Django

Back End

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Firebase

Data Storage

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Kafka

Message Broker

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AWS

Cloud & Oops

Impact & Results

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Automated Surveillance Monitoring

Reduced manual workload by automating the review of surveillance footage.

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Faster Incident Response

Improved reaction time with real-time violation detection and instant alert notifications.

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Centralized Security Oversight

Enhanced visibility and control through an integrated web-based monitoring dashboard.

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Scalable and High-Performance System

Designed to efficiently manage large-scale camera networks and high data volumes.

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