Intelligent Surveillance System

Problem

Traditional surveillance systems require constant human monitoring and generate large amounts of video data that is difficult to analyze efficiently. There's a need for intelligent systems that can automatically detect and analyze events in real-time.

Approach

I developed an intelligent surveillance system using deep learning for real-time object detection, tracking, and behavior analysis. The system processes video streams in real-time and provides alerts for specific events.

Key Components

1. Object Detection: YOLO-based model for detecting people, vehicles, and objects
2. Tracking: Multi-object tracking algorithm for following detected objects
3. Behavior Analysis: Rule-based and ML-based analysis of object behaviors
4. Alert System: Real-time notifications for predefined events

Technical Implementation

• Used TensorFlow for model training and inference
• OpenCV for video processing and computer vision tasks
• Optimized pipeline for real-time performance
• Scalable architecture for multiple camera feeds

Results

• Real-time Processing: Successfully processes video streams in real-time
• High Accuracy: Achieved high detection and tracking accuracy
• Scalability: System can handle multiple camera feeds simultaneously
• Practical Application: Deployed in real-world surveillance scenarios

Learnings

• Real-time computer vision system design
• Optimization techniques for video processing
• Integration of deep learning models into production systems
• Challenges of deploying ML systems in resource-constrained environments