Gesture Security System Using Mobile Device Metadata
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Solution Overview
Problem
Current video monitoring systems for security rely heavily on human operators, leading to inefficiencies and high rates of false positives in anomaly detection, especially as the number of cameras increases, limiting their effectiveness in real-time monitoring and response.
Innovation Solution
A gesture-based security system utilizing mobile devices to receive and analyze video feeds, incorporating non-gesture metadata such as location, image, and audio information to reduce false positives by selecting context profiles and aiding in the interpretation of recognized gestures, employing neural networks for dynamic recognition and learning algorithms to classify gestures as approved or unapproved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators monitor security video in real-time, then security monitoring is performed, but the system becomes inefficient and produces high false positive rates as the number of cameras increases
Solution Approach 1:
The patent replaces human operators with an automated computer-based system that uses machine learning models and algorithms to analyze video feeds from multiple cameras. This substitution eliminates human limitations in processing speed and accuracy while maintaining continuous monitoring capability, directly resolving the contradiction between detection reliability and monitoring efficiency.
Solution Approach 2:
The system performs self-analysis of video feeds using automated anomaly detection algorithms and machine learning models. The computer-based system independently processes and interprets video data without requiring human intervention for each analysis, enabling the system to serve itself in detecting and responding to security anomalies across multiple camera feeds simultaneously.
2Area of stationary object
If the number of cameras is increased to improve coverage, then security monitoring coverage is enhanced, but the ability of human operators to analyze all input is challenged
Solution Approach 1:
The patent replaces human analytical capacity with computer-based automated analysis systems that can process video feeds from numerous cameras simultaneously. The system uses machine learning models and algorithms to analyze multiple video streams in parallel, overcoming the human operator's limitation in handling increasing numbers of camera inputs while maintaining comprehensive coverage analysis.
Solution Approach 2:
The system dynamically scales its analytical capacity to match the number of active cameras. The automated analysis architecture can adaptively process varying numbers of video feeds without requiring additional human operators, allowing the monitoring coverage area to expand while maintaining consistent analysis capability through computational rather than human resources.
3Productivity
If automated anomaly detection is implemented in video recording, then real-time analysis is improved, but false positives and inaccurate results occur
Solution Approach 1:
The patent implements preliminary training phases where the machine learning models are trained on extensive datasets of normal and anomalous gestures before deployment. Context profiles are pre-established for different secured areas, and the system learns to distinguish between approved and unapproved gestures in advance. This preliminary action reduces false positives by ensuring the automated detection system is properly calibrated before real-time analysis begins.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results are continuously evaluated and used to refine the machine learning models. The automated anomaly detection system learns from its performance, adjusting its parameters and thresholds based on actual results to reduce false positives over time while maintaining high-speed automated analysis capability.
Data Source
AI summary
Using mobile devices in a gesture based security system is described. An image based feed is received from a camera incorporated in a first mobile device. The first mobile device is in communication with the gesture based security system. The camera has a view of one of a plurality of secured areas monitored by the gesture based security system. A gesture is recognized within the feed. Non-gesture metadata from the mobile device is associated with the recognized gesture. The non-gesture metadata is used to determine that the image based feed is a view of a first secured area of the plurality of secured areas. The determination whether the recognized gesture is an approved gesture within the first secured area is made according to non-gesture metadata associated with the recognized gesture.