Florida International University

Mohammadhadi Amini

Naphtali David Rishe

Khandaker Mamun Ahmed

Liwei Ding

Systems, methods, and frameworks are provided for privacy-aware weapon anomaly detection via integrated object recognition and skeletal motion analysis. This framework integrates real-time object detection and motion analysis to identify weapon anomalies in video surveillance while preserving privacy. The framework combines a fine-tuned object detection model, a head-segmentation module for anonymizing unarmed individuals, and a skeleton-based motion analysis module to detect threatening behaviors. By refining and fusing detection and motion analysis outputs, the framework enhances detection accuracy and reduces false positives, thereby providing a reliable solution for intelligent, privacy-preserving surveillance applications.

Systems and methods for privacy-aware weapon anomaly detection via integrated object recognition and skeletal motion analysis

Systems and methods are provided for detecting one or more anomalous events in video. Histogram-based noise cleansing, higher-order deep convolutional neural network-based feature extraction, instance segmentation, instance summation, difference calculation, and normalization can be used. Human-in-loop systems and methods can facilitate human decisions for anomaly detection. The decision of an anomaly event can be made by, for example, the instance difference value(s).

Growing community of inventors

Miami, FL, United States of America

Mohammadhadi Amini