Live Object Detection for Crime Prevention

Introduction

Our Live Object Detection system is an advanced AI-powered solution designed for real-time crime prevention and monitoring. Utilizing state-of-the-art deep learning models, including DETR, YOLOv5, YOLOv8, and Faster R-CNN, the system can accurately detect and classify suspicious activities. The objective is to enhance security measures by identifying potential threats, such as robbery, fights, explosions, shootings, accidents, and other suspicious activities, in real-time.

Features and Functionality

• Multi-Model Approach: Implemented DETR, YOLOv5, YOLOv8, and Faster R-CNN to evaluate and optimize object detection accuracy for crime-related activities.
• Real-Time Crime Detection: The system processes live surveillance footage and immediately detects potential threats, reducing response time for law enforcement.
• Roboflow-Labeled Dataset: Utilized a pre-annotated dataset from Roboflow, consisting of multiple crime-related object categories, eliminating the need for additional data preprocessing.
• High-Speed Inference: Optimized models for low-latency performance to ensure near-instantaneous detection.
• Custom Model Training: Fine-tuned models on crime-related scenarios to improve detection precision and minimize false positives.

Implementation

• Dataset Preparation:
  • Used Roboflow for dataset annotation and preprocessing.
  • Dataset includes classes like suspicious, accident, explosion, fight, robbery, and shooting.
• Model Selection and Training:
  • Experimented with DETR, YOLOv5, YOLOv8, and Faster R-CNN for comparative analysis.
  • Trained custom models on a crime-specific dataset to improve detection accuracy.
• Real-Time Inference Pipeline:
  • Integrated object detection models with OpenCV and TensorRT for optimized real-time performance.
  • Deployed on GPU-powered edge devices to ensure high-speed processing.
• Alerting and Response System:
  • Configured automated alerts via webhooks and APIs to notify security teams in real-time.
  • Potential threats trigger audio-visual alerts and notifications to law enforcement agencies.

Results

• Enhanced Crime Detection: Successfully identified and classified multiple crime-related activities in real-time.
• Optimized Accuracy: YOLOv8 outperformed other models in terms of speed and accuracy for real-world crime detection.
• Seamless Integration: The system can be easily integrated with CCTV surveillance networks, security control centers, and law enforcement databases.
• Scalability & Efficiency: Designed to scale across multiple surveillance cameras, ensuring city-wide monitoring capabilities.

Conclusion

Our Live Object Detection for Crime Prevention system leverages the power of AI-driven object detection models to enhance public safety and security. With real-time threat identification, rapid response mechanisms, and seamless integration with existing surveillance systems, this technology has the potential to revolutionize crime prevention efforts.