Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Behavior Analysis

Background:

• Detecting dangerous behaviors in stadiums is challenging due to variable video conditions and complex human actions.
• Traditional methods struggle with diverse features and intricate human movement patterns.

Purpose of the Study:

• To develop and analyze a risk warning model for stadium operations using a deep confidence neural network.
• To improve the accuracy and reliability of dangerous behavior detection in surveillance footage.

Main Methods:

• Extracted human behavior skeleton and optical flow features from surveillance videos.
• Developed a deep confidence neural network model utilizing skeleton sequences (18 joints per frame with confidence values) and temporal information.
• Incorporated optical flow information and temporal relational inference for enhanced recognition.

Main Results:

• The deep confidence neural network significantly outperformed manual feature extraction methods.
• The proposed method demonstrated superior performance in recognizing dangerous behaviors compared to other algorithms.
• Skeleton and optical flow features proved highly effective for behavior classification.

Conclusions:

• Deep confidence neural networks, leveraging skeleton and optical flow features, offer a robust solution for stadium safety risk assessment.
• This approach enhances the ability to identify and classify dangerous behaviors in real-time surveillance.
• The findings suggest a significant advancement in automated safety monitoring for public venues.