AI-Powered Fight Detection Using MediaPipe for Smart Surveillance
A Comprehensive Case Study on Revolutionizing Health Care System with AI Assistants
Project Goal
The primary goal of the Healthcare AI Assistant project is to develop an intelligent, voice-activated system that streamlines healthcare operations by providing dedicated AI assistants for doctors, nurses, patients, and administrators. This system aims to enhance efficiency, improve patient care, and optimise resource management through advanced automation and intelligent data handling.
Industry
Security and Surveillance
Location
San Francisco
Tech Team
Project Manager | AI and Computer Vision Engineer | MediaPipe Specialist | Dataset Curation Specialist | Full Stack Developer (React/Django) | User Experience (UX) Designer
Team Involved
- Project manager
- Data Scientists
- ML Engineers
- Risk Assessment Specialists
- Full Stack Engineers (React/Python)
- Compliance Officers
- UX Designer
Business Tasks the Client Wanted to Solve
Enhance Security Surveillance
- Real-time detection of fighting scenes to improve surveillance systems and enable quicker incident responses.
Custom Dataset Creation
- Develop a dataset tailored to various fighting styles and scenarios for precise classification.
Integration with Existing Systems
- Integrate the fighting scene detection module with pre-existing security systems.
KeyPoint Mapping for Better Accuracy
- Use Media Pipe to identify and map key points in human movements, focusing on patterns indicative of fights (e.g., punches, kicks).
Scalability for Large-Scale Deployment
- Ensure the system can handle multiple camera feeds simultaneously in diverse locations like malls, schools, and public spaces.
Business Tasks the Client Wanted to Solve:
1) Accelerate Loan Processing
- Reduce the time taken to process and approve loan applications through automation of manual review processes
- Enable real-time preliminary loan decisions
2) Enhance Risk Assessment
- Implement more sophisticated risk assessment models using multiple data points
- Improve the accuracy of default prediction
- Reduce human bias in the loan approval process
3) Change Any Particular Thing in the Generated Image
- Handle increasing application volumes without proportionally increasing staff
- Maintain consistency in loan evaluations across all applications
- Enable simultaneous processing of multiple applications.
4) Ensure Compliance
- Maintain transparent decision-making processes
- Provide clear audit trails for regulatory requirements
Implement fair lending practices
5 ) Improve Customer Experience
- Reduce waiting times for loan decisions
- Provide clear feedback on application status
- Enable digital document submission and verification
What pitfalls did the client face?
Lack of Standardized Datasets
Existing datasets were insufficient to cover diverse fighting scenarios.
Low Accuracy in Real-Time Detection
Previous systems failed to achieve high accuracy in detecting fighting scenes in varied lighting and environmental conditions.
Integration Challenges
Difficulties in integrating AI systems with legacy surveillance infrastructure.
Data Privacy Concerns
Ensuring that surveillance data remains secure and complies with privacy regulations.
What pitfalls did the client face?
1) Legacy System Integration
- Existing systems were not designed for AI integration
- Historical data was stored in various formats and locations
- Manual processes were deeply embedded in operations
2) Data Quality and Standardization
- Inconsistent data formats across different sources
- Missing or incomplete historical data
- Lack of standardized documentation processes.
3) Regulatory Compliance
- Need for explainable AI decisions
- Ensuring fair lending practices
- Meeting data privacy requirements
4) Staff Resistance
- Concerns about job security
- Reluctance to adopt new technologies
- Learning curve for new systems
What we suggested
Requirement Analysis and Planning
- Conduct workshops with security experts to understand common fighting patterns and scenarios.
- Define key functionalities for fighting scene detection, including multi-camera support and notification triggers.
System Design and Architecture
- Backend: Utilize Python, TensorFlow, and OpenPose for enhanced action recognition.
- Frontend: Develop a responsive web interface using React for real-time video feed monitoring and alerts.
Development and Integration
- Create a custom dataset with varied fighting scenes using public and proprietary video sources.
- Leverage MediaPipe for keypoint extraction and map movement patterns to identify fight-related activities.
- Train a classification model using Convolutional Neural Networks (CNNs) combined with Long Short-Term Memory (LSTM) for sequence analysis.
Deployment and Continuous Improvement
- Deploy the system using cloud services like AWS for scalability and real-time processing.
- Implement a feedback loop from users to continuously refine the model.
- Added the Amazon API Gateway for smoother interactions
What we suggested:
1. Initial Assessment and Planning
- Conduct thorough analysis of existing loan approval processes
- Identify key pain points and automation opportunities
- Define success metrics and compliance requirements
- Create implementation roadmap
2. System Design and Architecture
- Backend: Use Python and TensorFlow for AI model development. Leverage cloud services for scalability and data management.
- Frontend: React-based dashboard for loan officers
- Database: MongoDB for flexible data storage
- API Layer: FastAPI for high-performance backend services
3. Development and Integration
- Implement modular AI components for different aspects of loan processing
- Create explainable AI features for transparency
- Develop real-time monitoring and alerting systems
- Build robust data validation and cleaning pipelines
4. Testing and Deployment
- Conduct parallel testing with existing systems
- Implement gradual rollout strategy
- Provide comprehensive staff training
- Establish feedback loops for continuous improvement
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Technical Architecture
Keypoint Detection
- MediaPipe library for precise human pose tracking and movement analysis.
Backend Framework
- Django for server-side logic and integration with TensorFlow models.
Frontend Framework
- React for a dynamic, real-time monitoring dashboard.
Cloud Infrastructure
- AWS for scalable processing of multiple video feeds and data storage.
Data Privacy and Security
- End-to-end encryption for surveillance data, with role-based access controls.
Technical architecture:
1. AI/ML Stack
- TensorFlow for deep learning models
- scikit-learn for traditional ML algorithms
- XGBoost for gradient boosting
- SHAP for model explainability
2. Backend Framework
- FastAPI for high-performance API development
- Celery for task queue management
- Redis for caching
- MongoDB for document storage
3. Frontend Framework
- T React with TypeScript
- Redux for state management
- Material-UI for component library
- D3.js for data visualization
4. Cloud Services
- AWS ECS for containerized applications
- AWS Lambda for serverless functions
- Amazon S3 for document storage
- Amazon RDS for relational data
5. Security and Compliance
- AWS KMS for encryption
- OAuth 2.0 for authentication
- Regular security audits
- Automated compliance checking
6. Monitoring and Analytics
- ELK Stack for log management
- Prometheus for metrics
- Grafana for dashboards
- Custom analytics for model performance
Business Outcomes
Enhanced Security
- Faster response times due to real-time detection and alerts.
Improved Accuracy
- Achieved over 95% accuracy in detecting diverse fighting scenarios.
Scalable Solution
- Successfully deployed in high-traffic areas like airports and shopping malls.
User-Friendly Interface
- Easy-to-use dashboard with intuitive controls for security personnel.
Compliance with Regulations
- Robust data privacy measures ensured compliance with global standards.
Business Outcomes:
1. Operational Efficiency
- 40% reduction in loan processing time
- 60% decrease in manual document review
- 85% automation of routine tasks
2. Risk Management
- 25% improvement in risk assessment accuracy
- 30% reduction in default rates
- Enhanced fraud detection capabilities
3. Customer Satisfaction
- 50% faster loan decisions
- 70% reduction in application errors
- Improved transparency in decision-making
4. Scalability
- 3x increase in application processing capacity
- 45% reduction in operational costs
- Improved resource utilization
5. Compliance and Reporting
- 100% audit trail coverage
- Automated compliance reporting
- Reduced regulatory risks