Context Detection Processor for Security Video Analysis
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Solution Overview
Problem
Existing security systems are inadequate in protecting authorized users during entry or exit, as they do not effectively detect and respond to threats such as duress or unauthorized access attempts beyond mere motion detection.
Innovation Solution
A security system incorporating video monitoring and context detection processors that analyze video frames to identify specific human contexts and threats, such as duress, gestures, sounds, and biometric parameters, and can send silent alarms to a central monitoring station without compromising user safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional motion detection sensors are used, then the security system can detect intruders, but it cannot effectively detect threats to authorized users during entry or exit
Solution Approach 1:
The system divides the detection function into multiple specialized processors: a human detection processor that identifies human figures, a gesture detection processor that analyzes specific movements, and a context detection processor that evaluates threat situations. This segmentation allows each processor to specialize in detecting specific aspects of security threats, enabling reliable detection of threats to authorized users while maintaining adaptability to various threat contexts.
Solution Approach 2:
The system introduces video cameras as an intermediary between the intruder and the alarm system. The cameras capture visual information that is then processed by multiple detection processors to identify human figures, gestures, and threat contexts. This intermediary enables the system to detect specific threat situations involving authorized users without requiring direct physical contact or proximity sensors.
2Measurement precision
If video monitoring with context detection is implemented, then detection accuracy for specific threats improves, but system complexity increases
Solution Approach 1:
The complex detection task is segmented into multiple specialized processors, each responsible for a specific detection function. The human detection processor identifies human figures, the gesture detection processor analyzes specific movements, and the context detection processor evaluates threat situations. This segmentation improves measurement precision for each specific detection task while organizing system complexity into manageable, modular components.
Solution Approach 2:
The system employs self-service through automated video analysis and context evaluation. The processors automatically analyze video frames, detect human figures, identify gestures, and determine threat contexts without requiring manual intervention. This self-service capability improves detection accuracy while reducing the operational complexity of managing the system.
Data Source
AI summary
Systems and methods for detecting personal distress and gesture commands in security video are provided. Some methods can include receiving a sequence of images from a video device monitoring a secured area, analyzing the sequence of images to detect a presence of a human in the sequence of images, when the presence of the human is detected in the sequence of images, analyzing the sequence of images to detect one of a plurality of contexts that requires an action in the sequence of images, and when the one of the plurality of contexts that requires the action is detected in the sequence of images, transmitting a signal to execute the action. Each of the plurality of contexts can include a respective act performed by the human or a respective condition of the human irrespective of the human being in motion or being stationary.