Privacy-Aware Weapon Anomaly Detection With Late Fusion
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Solution Overview
Problem
Conventional video anomaly detection systems struggle with false positives and lack contextual awareness in dynamic environments, particularly in identifying weapon-related threats due to the variability of human motion and the absence of object detection capabilities.
Innovation Solution
Integrates real-time object detection with skeletal motion analysis, employing a fine-tuned model to detect weapons and humans, anonymizes unarmed individuals, and applies a late fusion technique to refine anomaly scores, optimizing computational efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional weapon detection systems are deployed in dynamic public environments, then weapon detection capability is provided, but false positives increase and reliability decreases
Solution Approach 1:
The patent combines object detection (weapon presence) with skeleton-based motion analysis into a unified anomaly detection system. The object detection model identifies weapons while the diffusion model analyzes human motion patterns, and their results are fused through a refinement module to produce final anomaly scores, thereby improving reliability by cross-validating multiple detection modalities
Solution Approach 2:
The feature refinement module serves as an intermediary that processes and reconciles the outputs from object detection and motion analysis. It applies filtering operations to weapon confidence scores and anomaly scores, merging these intermediate results into a cohesive final anomaly score that reduces false positives while maintaining detection accuracy
2Measurement precision
If skeleton-based motion analysis is used to infer behavioral intent, then anomaly detection capability is improved, but false positives increase due to misclassification of normal movements
Solution Approach 1:
The system merges object detection results with skeleton-based motion analysis, where the object detection model provides context about weapon presence that helps disambiguate normal movements from threatening ones. By combining these modalities, the system reduces false positives while maintaining behavioral analysis accuracy
Solution Approach 2:
The feature refinement module implements feedback mechanisms where weapon confidence scores from object detection inform the interpretation of motion analysis results. The system continuously refines anomaly scores based on feedback from both detection modalities, improving classification reliability by cross-validating findings
3Measurement precision
If object detection and motion analysis are integrated, then threat discrimination improves, but computational complexity and processing time increase
Solution Approach 1:
The system segments the anomaly detection task into distinct modular components: object detection module, skeleton extraction module, diffusion model for motion analysis, and feature refinement module. Each module handles a specific aspect of the detection process, making the overall complex system manageable and allowing parallel processing to reduce computational bottlenecks
4Speed
If real-time processing is implemented in high-traffic environments, then responsiveness is improved, but computational load and processing complexity increase
Solution Approach 1:
The detection pipeline is segmented into independent processing stages that can operate in parallel: object detection runs simultaneously with skeleton extraction, and both feed into the diffusion model. This segmentation enables efficient utilization of computational resources while maintaining real-time processing speed in high-traffic environments
Solution Approach 2:
The system applies selective processing where the full diffusion model computation is triggered only when object detection identifies potential weapons. For frames without detected weapons, the system uses simplified processing paths, reducing computational load while maintaining responsiveness for actual threats
Data Source
AI summary
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.