AI for Home Security: Package Theft Detection with YOLOv11 Pose Estimation & Object Detection 🚨
Porch piracy is becoming a significant issue as online shopping continues to grow. Instead of just relying on motion sensors, I’m working on a multi-model AI pipeline that leverages computer vision to detect both packages and suspicious human behavior in real-time.
🧠 Core Architecture:
YOLOv11 Object Detection → Trained to detect parcels/packages left at the doorstep.
YOLOv11 Pose Estimation → Fine-tuned to analyze body keypoints and classify suspicious vs. thiefting behavior.
Multi-Stream Fusion → Outputs from both models are combined to infer higher-level events.
⚙️ Pipeline Implementation:
Framework: Ultralytics YOLOv11
Backend: Python + OpenCV for real-time video processing
Input: Doorbell or CCTV camera footage (.mp4 or live RTSP streams)
Output: Annotated video with bounding boxes, keypoints, and event labels
Next Step: Adding alerting mechanisms (SMS, email, or smart-home integration) when theft is suspected
📊 Technical Challenges & Learnings:
Designing a robust annotation strategy for “suspicious” & "thiefting" actions, since pose-based datasets are scarce.
Balancing false positives (normal neighbor passing by) vs. false negatives (actual theft).
Optimizing inference for edge devices (Raspberry Pi / NVIDIA Jetson Nano) to run real-time detection on doorbell cameras.
💡 This project highlights how hybrid vision systems (detection + pose estimation) can provide context-aware AI for real-world security applications. I believe these multi-model approaches are a big step toward smarter and safer homes.