Seer Vision AI (VisLangStream)

Intelligent Real-Time Video Analysis through Natural Language Processing

What is Seer Vision AI?

Seer Vision AI (VisLangStream) is an innovative real-time video analysis platform that revolutionizes how we interact with surveillance and monitoring systems. Instead of requiring complex programming or technical expertise, users can simply ask questions in plain English and receive intelligent, contextually-aware answers about what's happening in their video feeds.

The Problem We Solve

Traditional video surveillance systems face several critical limitations:

Technical Complexity: Setting up object detection requires programming skills and computer vision expertise
Static Analysis: Most systems only detect pre-programmed objects or events
Lack of Context: Existing solutions analyze frames in isolation without understanding temporal relationships
Limited Accessibility: Non-technical users struggle to extract meaningful insights from video data
Integration Challenges: Difficult to integrate analysis results with external business systems

Our Solution

Seer Vision AI transforms video analysis through three core innovations:

1. Conversational Video Analysis

Instead of configuring complex detection rules, users simply type natural language questions:

"How many people are wearing safety helmets?"
"Is the parking lot full or empty?"
"Are there any delivery trucks at the loading dock?"
"What color shirts are the workers wearing?"

2. Intelligent Memory System

Unlike traditional frame-by-frame analysis, our system maintains contextual memory:

Temporal Awareness: Understands changes over time ("Has anyone left the building in the last 10 minutes?")
Conversation Continuity: Builds upon previous responses for more accurate analysis
Scene Understanding: Maintains a comprehensive understanding of the environment

3. Enterprise Integration

Seamlessly connects with existing business systems:

Webhook Exports: Automatically send analysis results to external systems
Real-time APIs: Integrate with dashboards, alerting systems, and databases
Flexible Formats: Output results in JSON or plain text formats

How It Works

The User Experience

Seer Vision AI transforms complex video analysis into a simple conversation:

Connect Your Cameras: Add USB cameras or network streams to the system
Ask Questions: Type natural language queries about what you want to monitor
Get Real-Time Answers: Receive intelligent responses with confidence scores
Track Over Time: Enable memory mode for contextual, time-aware analysis
Export Results: Configure webhooks to automatically send results to your systems

Core Functionality

Intelligent Frame Analysis

The system continuously captures frames from connected cameras and processes them through advanced vision-language models. Each frame is analyzed in the context of user-defined prompts, generating human-readable responses that answer specific questions about the visual content.

Contextual Understanding

When memory mode is enabled, the system maintains a sophisticated understanding of the scene across multiple frames. This allows for queries like:

"Has the number of people increased since my last check?"
"What changes have occurred in the last 5 minutes?"
"Are the same people still present?"

Real-Time Processing Pipeline

Frame Capture: Optimized frame extraction from video streams
Preprocessing: Image optimization for AI model consumption
AI Analysis: Vision-language model processing with custom prompts
Context Integration: Memory system enhances responses with temporal awareness
Result Delivery: Formatted responses with confidence scores and metadata

Adaptive Performance

The system automatically adjusts processing parameters based on:

System Load: Dynamic queue management prevents resource conflicts
Analysis Interval: Configurable timing from 10-120 seconds per analysis
Camera Capabilities: Optimized processing for different camera types
User Requirements: JSON vs. plain text output formatting

Real-World Applications

🏢 Business & Retail

Customer counting and behavior analysis
Queue management and wait time optimization
Inventory monitoring and stock level alerts
Employee safety compliance monitoring

🏭 Industrial & Manufacturing

Worker safety equipment compliance
Production line monitoring and quality control
Equipment status and maintenance alerts
Workplace safety incident detection

🏫 Education & Healthcare

Classroom occupancy and engagement monitoring
Patient monitoring and care compliance
Facility utilization tracking
Emergency response and safety protocols

🏠 Smart Buildings & Security

Access control and visitor management
Parking space availability tracking
Maintenance and cleaning verification
Energy usage optimization through occupancy detection

Core Features Deep Dive

🎯 Natural Language Video Analysis

The heart of Seer Vision AI is its ability to understand and respond to natural language queries about video content. Powered by advanced Large Language and Vision Assistant (LLaVA) models, the system can:

Understand Complex Queries: Process multi-part questions requiring visual reasoning
Provide Detailed Responses: Generate comprehensive answers with specific details
Maintain High Accuracy: Deliver confidence-scored results for quality assurance
Handle Ambiguity: Interpret unclear queries and provide clarifying responses

🧠 Contextual Memory System

Our proprietary memory system sets Seer Vision AI apart from traditional video analysis:

Scene Continuity: Maintains understanding of the environment across multiple frames
Change Detection: Automatically identifies and reports significant changes
Temporal Queries: Answer questions about events over time periods
Conversation Memory: Builds upon previous interactions for more accurate responses

📊 Comprehensive Analytics Dashboard

Transform raw video data into actionable business insights:

Real-Time Metrics: Live confidence scores, processing times, and system performance
Historical Trends: Analyze patterns over hours, days, weeks, or months
Camera Performance: Monitor individual camera effectiveness and optimization opportunities
Query Analytics: Track most common questions and response accuracy

🔗 Webhook Integration & Export

Enterprise-ready integration capabilities:

Automated Notifications: Send analysis results to external systems in real-time
Flexible Formats: Choose between structured JSON or human-readable text
Secure Delivery: HMAC signature verification for webhook security
Retry Logic: Robust delivery mechanisms with automatic retry on failure

Academic Research Context

Developed as part of advanced research at the University of Birmingham's School of Computer Science, Seer Vision AI represents a paradigm shift in video analysis technology. This Master's research project explores the intersection of computer vision and natural language processing, demonstrating how advanced AI can be made accessible to non-technical users while maintaining enterprise-grade performance.

Research Contribution: This project contributes to the field of Human-Computer Interaction in AI systems, specifically addressing the usability gap in computer vision applications and proposing novel approaches to contextual video understanding.

Technical Innovation Summary

🎯 Multi-Modal AI Integration

Seamless fusion of vision and language models for comprehensive scene understanding
Real-time processing capabilities with sub-second response times
Advanced prompt engineering for optimal model performance

🧠 Novel Memory Architecture

Proprietary temporal context system that maintains scene understanding across frames
Intelligent similarity detection to avoid redundant processing
Dynamic buffer management optimized for different analysis intervals

📊 Performance Optimization

Intelligent queuing system preventing resource conflicts
Adaptive frame processing based on system load
Comprehensive metrics collection for continuous improvement

🔗 Enterprise-Ready Architecture

Scalable microservices design supporting multiple concurrent streams
Robust authentication and authorization systems
Comprehensive API design following RESTful principles

System Architecture

Seer Vision AI implements a sophisticated 6-layer architecture designed for scalability, maintainability, and performance:

1. Presentation Layer (React + TypeScript)

Modern, responsive web interface built with React 18
Real-time video streaming and analysis visualization
Comprehensive dashboard for analytics and system monitoring
Mobile-responsive design with adaptive layouts

2. API Gateway Layer (Express.js)

RESTful API endpoints with comprehensive error handling
JWT-based authentication and authorization
Request validation and rate limiting
CORS configuration for secure cross-origin requests

3. Business Logic Layer (Node.js Services)

Camera management and stream orchestration
User authentication and session management
Analytics aggregation and reporting
Webhook configuration and delivery

4. Processing Layer (LLaVA Integration)

Advanced frame analysis using state-of-the-art vision-language models
Intelligent queuing system for optimal resource utilization
Context-aware processing with memory integration
Performance optimization through caching strategies

5. External Integration Layer

Ollama LLaVA model integration for AI processing
Webhook delivery system for external notifications
Extensible architecture for future AI model integration

6. Data Layer (SQLite)

Optimized database schema for video analytics
Efficient indexing for time-series data queries
Comprehensive logging and audit trails
Automated backup and recovery procedures

Installation & Setup

Prerequisites

Before setting up Seer Vision AI, ensure you have the following installed:

Node.js (v18 or higher) - Download here
npm (v8 or higher) - Comes with Node.js
Ollama - Installation guide
Git - Download here

Step 1: Clone the Repository

git clone https://github.com/your-username/VisLangStream.git
cd VisLangStream

Step 2: Install Dependencies

The project uses a monorepo structure with separate frontend and backend dependencies:

# Install root dependencies and setup both client and server
npm install

# This will automatically run:
# - npm install in the client directory
# - npm install in the server directory

Step 3: Configure Ollama

Install and configure the LLaVA model:

# Install Ollama (if not already installed)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull the required LLaVA model
ollama pull llava:latest

# Verify installation
ollama list

Step 4: Environment Configuration

Create environment configuration files:

# Server environment
cd server
cp .env.example .env

Edit the .env file with your configuration:

NODE_ENV=development
PORT=3001
DATABASE_PATH=./database.sqlite
JWT_SECRET=your-secure-jwt-secret
JWT_REFRESH_SECRET=your-secure-refresh-secret
OLLAMA_BASE_URL=http://localhost:11434

Step 5: Initialize Database

The database will be automatically initialized when you first run the server:

cd server
npm run dev

Step 6: Start the Application

Open two terminal windows:

Terminal 1 - Start the backend server:

cd server
npm run dev

Terminal 2 - Start the frontend client:

cd client
npm run dev

Or use the convenient combined command from the root directory:

npm start

Step 7: Access the Application

Frontend: http://localhost:5173
Backend API: http://localhost:3001
Ollama: http://localhost:11434

Usage

1. Camera Setup

Navigate to the "Cameras" section
Click "Add Camera" and select USB camera
Configure camera settings including analysis intervals
Test camera connection to ensure proper setup

2. Real-Time Analysis

Select a configured camera from the dashboard
Enter natural language prompts (e.g., "Count people in the frame")
Start analysis to receive real-time responses
Monitor confidence scores and processing metrics

3. Memory Context

Enable memory mode for contextual analysis
System maintains conversation history across frames
Reduces redundant processing and improves accuracy
Ideal for tracking changes over time

4. Analytics & Monitoring

Access comprehensive analytics dashboard
View detection trends and confidence metrics
Monitor camera performance and system health
Export data for further analysis

5. Webhook Export

Configure webhook endpoints in the Connections section
Set up automated result forwarding
Choose between JSON and plain text formats
Test webhook connectivity before deployment

Technology Stack

Frontend

React 18 - Modern component-based UI framework
TypeScript - Type-safe JavaScript development
Tailwind CSS - Utility-first CSS framework
Shadcn/ui - High-quality component library
Recharts - Data visualization and analytics
React Router - Client-side routing
Axios - HTTP client for API communication

Backend

Node.js - JavaScript runtime environment
Express.js - Web application framework
SQLite - Embedded database for data persistence
JWT - JSON Web Tokens for authentication
bcrypt - Password hashing and security
Multer - File upload handling

AI & Processing

LLaVA (Large Language and Vision Assistant) - Vision-language model
Ollama - Local AI model deployment platform
Canvas API - Frame processing and manipulation

Development Tools

Vite - Fast build tool and development server
ESLint - Code linting and quality assurance
Prettier - Code formatting
Concurrently - Run multiple processes simultaneously

Project Structure

VisLangStream/
├── client/                     # Frontend React application
│   ├── src/
│   │   ├── api/               # API client functions
│   │   ├── components/        # Reusable UI components
│   │   │   ├── dashboard/     # Dashboard-specific components
│   │   │   ├── connections/   # Webhook and export components
│   │   │   └── ui/           # Base UI components
│   │   ├── contexts/         # React context providers
│   │   ├── hooks/            # Custom React hooks
│   │   ├── lib/              # Utility functions
│   │   ├── pages/            # Page components
│   │   └── main.tsx          # Application entry point
│   ├── public/               # Static assets
│   └── package.json          # Frontend dependencies
│
├── server/                    # Backend Node.js application
│   ├── config/               # Configuration files
│   │   └── database.js       # Database setup and migrations
│   ├── models/               # Data models and database interactions
│   │   ├── Camera.js         # Camera model
│   │   ├── User.js           # User authentication model
│   │   ├── VideoAnalysis.js  # Analysis tracking model
│   │   └── LiveResult.js     # Analytics data model
│   ├── routes/               # API route definitions
│   │   ├── authRoutes.js     # Authentication endpoints
│   │   ├── cameraRoutes.js   # Camera management endpoints
│   │   ├── videoAnalysisRoutes.js # Analysis endpoints
│   │   └── analyticsRoutes.js # Analytics endpoints
│   ├── services/             # Business logic services
│   │   ├── llavaService.js   # AI model integration
│   │   ├── memoryService.js  # Context management
│   │   ├── cameraService.js  # Camera operations
│   │   └── webhookService.js # Webhook delivery
│   ├── utils/                # Utility functions
│   └── server.js             # Server entry point
│
└── package.json              # Root project configuration

Contributing

This project was developed as part of advanced research at the University of Birmingham. While primarily an academic research project, contributions and feedback are welcome from the research community.

Development Guidelines

Code Quality: Maintain high code quality standards with comprehensive testing
Documentation: Document all new features and API changes
Academic Integrity: Respect the academic nature of this research project
Performance: Ensure all contributions maintain system performance standards

Research Collaboration

For academic collaborations, research partnerships, or citing this work, please contact the research team through the University of Birmingham's Computer Science department.

License

This project is developed for academic research purposes at the University of Birmingham. The codebase is provided for educational and research use. Commercial use requires explicit permission from the research team.

Academic Use: Freely available for academic research and educational purposes Commercial Use: Contact the research team for licensing arrangements Attribution: Please cite this work in academic publications when applicable

Developed at the University of Birmingham
School of Computer Science
Advanced Research in Computer Vision and Natural Language Processing

For technical support, research inquiries, or collaboration opportunities, please refer to the project documentation or contact the development team through the university's official channels.

Name		Name	Last commit message	Last commit date
Latest commit History 17 Commits
client		client
server		server
.gitignore		.gitignore
README.md		README.md
Seer Vision AI - Vislangstream.pdf		Seer Vision AI - Vislangstream.pdf
package-lock.json		package-lock.json
package.json		package.json

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