AI Vision Systems for Theft and Shoplifting Detection in Retail

This project leverages AI-powered computer vision systems to detect theft and shoplifting behavior in real time within retail environments. Using surveillance video feeds, the system identifies suspicious activities based on human pose, object interactions, and movement patterns — allowing proactive loss prevention.

Retail stores face consistent losses due to shoplifting and internal theft, which are often difficult to catch in real-time:
Human monitoring is error-prone and expensive
Suspicious behaviors are subtle and context-dependent.
Losses due to theft significantly impact profit margins, especially in high-footfall stores

The proposed solution is an AI-powered surveillance system that uses video input from in-store cameras to:
Analyze human actions and gestures.
Detect potential theft behavior.
Alert staff or security in real-time.
Key Capabilities:
Detects behaviors like concealing items, frequent shelf interactions, loitering, or unauthorized access
Uses pose estimation, object tracking, and anomaly detection models.
Can work with existing CCTV infrastructure in stores.

Video Input: Real-time CCTV footage from the retail environment
Frame Analysis:Human pose detection to track body language & Object interaction detection to monitor product pickup/concealment.
Behavior Classification:Trained on datasets of normal vs. suspicious shopping behavior & Uses temporal data to identify anomalies (e.g., lingering without checkout).
Alert System:Flags high-risk behavior and notifies staff via a dashboard or mobile app.

Differentiating between legitimate and suspicious behavior (e.g., comparing browsing vs. loitering)
Handling occlusions and camera blind spots
Minimizing false positives that could lead to customer discomfort
Ensuring the system works under different lighting and crowd density