Real-Time Multi-Person Suspicious Activity Detection using YOLOv11 Pose Estimation 🚨
🚨 Real-Time Multi-Person Suspicious Activity Detection using YOLOv11 Pose Estimation 🚨
I’m excited to share my latest AI-powered video analytics system, which takes security monitoring to the next level by detecting suspicious actions in real-time from CCTV footage, even in crowded scenes.
🔹 Key Features:
Advanced multi-person pose estimation with a custom-trained YOLOv11 model.
Real-time detection of normal vs. suspicious actions with temporal smoothing for high accuracy.
Professional video overlays — bounding boxes, pose skeletons, FPS counter, and alert banners.
Dynamic red flashing alarms for immediate attention when suspicious activity is detected.
Clean UI with logos, footers, and polished visualization.
An efficient processing pipeline for live streams or pre-recorded videos.
💡 Why this matters:
This system is designed for security and surveillance applications, enabling early intervention to help prevent theft, vandalism, or unsafe behavior. With precise pose estimation and action classification, security teams can react faster and smarter.
🛠️ Tech Stack & Tools:
Python • OpenCV • PyTorch • Ultralytics YOLOv11 • Pose Estimation • Real-Time Video Processing • Post-Processing Filters
If you’re passionate about AI for safety, surveillance, or sports analytics, I’d love to connect and exchange ideas. 🤝