XVeillance Unique AI Model

XVeillance is an advanced AI-powered shoplifting detection system that combines real-time object tracking with behavioral analysis to identify suspicious actions in retail environments accurately. The system monitors movement patterns through ByteTrack, flagging unusual behavior such as item concealment or loitering. When such events occur, short video clips are automatically extracted and analyzed by a 3D Convolutional Neural Network trained to recognize shoplifting gestures over time. This two-stage approach—fast tracking followed by deep temporal analysis—enables XVeillance to deliver precise alerts with minimal false positives, providing store managers with real-time video evidence and insights through a mobile or web dashboard.

How XVeillance Technology Platform Works

1. Real-time Person Detection, Gesture-Based Shoplifting Detection & Face Recognition

A real-time video processing pipeline begins by capturing and pre-processing CCTV streams, extracting frames every 100 milliseconds to balance speed and accuracy. Image enhancement techniques—such as denoising and contrast adjustment—optimize visibility, enabling reliable face detection even under poor lighting or noisy conditions. In parallel, advanced gesture recognition algorithms analyze human movements, identifying suspicious behaviors like concealing items in clothing or backpacks. This combined analysis enables both accurate identification and early detection of shoplifting actions, preparing enriched data for immediate alerting and downstream processing.

2. Behavioral Cross-Validation

After pre-processing, the AI-powered XVeillance algorithm detects gestures and faces in real time. Detected gestures and faces are then cropped and aligned for consistency, enhancing recognition accuracy. Our deep learning model converts these images into unique numerical embeddings, allowing fast and reliable identification across frames. This streamlined process ensures precise face recognition, even in dynamic environments.

3. Image Comparison with Registered Images Using Deep Learning

XVeillance maintains a database of registered shoplifting behaviors and faces. When a new image embedding is extracted, it is compared against the database using similarity measures such as a deep 3D neural network. If the similarity score exceeds a predefined threshold (e.g., 0.8), the system confirms a match, enabling seamless identification and real-time alerts when necessary.

4. Real-Time Mobile Alerts for Face Matches & Suspicious Gestures

When a match is detected—whether from facial recognition or gesture-based shoplifting behavior—the system immediately triggers an alert. Each alert includes vital metadata: the detected face image, gesture snapshots, confidence scores, timestamp, camera ID, and store location. These alerts are delivered in real-time to security staff through a dedicated mobile app or web dashboard, enabling swift on-site intervention. Meanwhile, corporate administrators can access a centralized web application with detailed reports and behavior analytics, supporting organization-wide oversight and proactive loss prevention strategies.