project matter

1. INTELLIGENT VIDEO SURVEILLANCE
USING DEEP LEARNING
• Final Year Project
• Using Deep Learning Techniques

2. Abstract
• Detects abnormal activities in surveillance
videos
• Uses Deep Learning (Spatial Autoencoder)
• Supports real-time and uploaded video
detection
• Abnormal frames saved for review

3. Introduction
• Domain: Artificial Intelligence & Computer
Vision
• Used for security monitoring
• Automates abnormal activity detection
• Overcomes limitations of manual surveillance

4. Problem Statement
• Manual surveillance is inefficient
• Delayed response to abnormal events
• High human effort required
• Need for automated intelligent system

5. Existing System
• Manual video monitoring
• Requires continuous human attention
• Prone to errors
• Time-consuming

6. Disadvantages of Existing System
• Low efficiency
• High manpower required
• Delayed detection
• Not scalable

7. Proposed System
• Automated video surveillance
• Deep learning-based detection
• Detects violence or abnormal behavior
• Stores abnormal frames automatically

8. Advantages of Proposed System
• Real-time detection
• High accuracy
• Reduced human effort
• User-friendly interface

9. Modules
• User Module
• Video Upload Detection
• Real-Time Camera Detection

10. Technologies Used
• Frontend: HTML, CSS, JavaScript
• Backend: Python (Django)
• Database: MySQL
• Algorithm: Spatial Autoencoder

11. Hardware Requirements
• Intel i3 or higher
• 4GB RAM or more
• 100GB Hard Disk

12. Software Requirements
• Python
• Django Framework
• XAMPP Server
• Sublime Text Editor

13. Applications
• Public surveillance systems
• Banks and ATMs
• Shopping malls
• Security monitoring

14. Conclusion
• Efficient abnormal activity detection
• Reduces human effort
• Improves security
• Suitable for real-time deployment

15. Future Scope
• Cloud deployment
• Mobile application
• AI model enhancement
• Multi-camera support

16. Thank You
• Thank You
• We are happy to answer your questions