Violence Detection System Using Segmented ML Circuits
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Solution Overview
Problem
Current computer vision systems for violence detection in surveillance face challenges with high resource requirements and low sensitivity, leading to either low accuracy or high false alarm rates, especially when processing large video streams.
Innovation Solution
A real-time human behavior detection system combined with object classification, utilizing reduced versions of machine learning techniques like YOLO and C3D, to enhance violence detection with separate weapon and violent behavior detection circuitry, providing a synergistic approach for accurate and efficient violence detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard machine learning techniques (YOLO, C3D) are used for violence detection, then detection accuracy is improved, but resource consumption increases
Solution Approach 1:
The system divides the violence detection task into separate specialized modules: weapon detection circuitry and violent behavior detection circuitry. Each module processes specific aspects independently, reducing the computational burden on each individual component while maintaining overall detection accuracy.
Solution Approach 2:
The patent employs reduced versions of machine learning techniques (reduced YOLO, reduced C3D) that perform partial processing sufficient for real-time surveillance applications. This partial action approach achieves adequate detection accuracy without the full computational overhead of complete algorithms.
2Measurement precision
If standard machine learning techniques are used for violence detection, then detection accuracy is improved, but processing speed decreases
Solution Approach 1:
By segmenting the detection task into parallel specialized circuits (weapon detection and violent behavior detection), the system processes multiple aspects simultaneously rather than sequentially, significantly improving processing speed while maintaining comprehensive detection capability.
Solution Approach 2:
The reduced machine learning techniques perform sufficient processing for real-time detection without unnecessary computational steps, achieving processing speeds suitable for real-time surveillance while maintaining adequate detection accuracy.
3Reliability
If comprehensive violence detection is implemented, then detection sensitivity is improved, but false alarm rate increases
Solution Approach 1:
The system separates weapon detection and violent behavior detection into independent specialized modules. This segmentation allows each module to focus on its specific detection task with optimized criteria, reducing cross-contamination of detection signals that lead to false alarms while maintaining high sensitivity for actual threats.
Data Source
AI summary
Disclosed herein are technologies for using computer vision for detection of violence, foreseeable, or imminent violence. The technologies can include a real-time human behavior detection system combined with object classification, which is to be used as an intelligent augmentation of security surveillance systems. The technologies can be used with security cameras, surveillance systems or unmanned aerial vehicles. The technologies can use various types of machine learning to enhance the technologies' violence detection. Also, the technologies can use a synergistic approach of combining different computer vision and machine learning technologies to provide highly accurate results.