🧠 Project Overview
The primary objective of this project is to develop a robust object detection system designed to identify anomalies among students, using Covenant University as a case study with some base detection classes.
Object detection is a critical technology that underpins a wide range of modern applications, including:
- Surveillance systems
- Security monitoring
- Autonomous vehicles
Its role in enhancing safety, security, and operational efficiency is indispensable. This project demonstrates the power of AI in real-world scenarios where anomaly detection can prevent incidents and improve overall campus safety.
⚙️ Tech Stack
- Frontend: Next.js
- Programming Languages: Python, TypeScript
- Machine Learning Frameworks: TensorFlow, PyTorch
- Object Detection Models: YOLO, Faster R-CNN
- Data Handling: OpenCV, NumPy, Pandas
- Deployment: Vercel
- Visualization: Matplotlib, Seaborn, and custom dashboard
🏗 Features & Capabilities
- Detects unusual or unexpected student behaviors in video feeds
- Supports multiple base detection classes for common objects/events
- Real-time alerts for anomalies
- Data visualization for tracking and analysis
- Scalable architecture for large datasets
🚀 Challenges & Learnings
- Handling imbalanced datasets for anomaly detection
- Reducing false positives while maintaining high detection accuracy
- Optimizing inference speed for real-time video streams
- Integrating computer vision models into a functional monitoring system
## 📸 Gallery
🔗 Links
- Repository: https://github.com/Proac-Tee/object_detection
- Live Demo: cu-detection
- Author: Babatunde Taiwo
🏁 Summary
The Anomaly Detection App leverages advanced object detection algorithms to enhance security and operational safety. By applying AI to real-world campus scenarios, it provides actionable insights and real-time monitoring, illustrating the transformative impact of machine learning in anomaly detection.