Deep Learning-Based Supervision for Enhanced Self-Awareness under Widespread Video Surveillance: Psychological and Social Consequences

Modern humans are healthy because they use their communities to boost their self-esteem by learning to see themselves as contributing members of a larger whole. Our interactions with people are influenced by several things, including the way that people feel about themselves and the manner in kind o...

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Bibliographic Details
Published in2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS) pp. 1 - 6
Main Authors Hassan, Ahmed R., Sheel, Shaid, Haroon, Noor Hanoon, Ismail, Laith S., Diame, Tamarah Alaa, Jawad, Israa Abed
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.11.2023
Subjects
Analytical models
Data models
Deep learning
Object recognition
Real-time systems
self-awareness
social management
Social-psychological data
Video surveillance
Video surveillance system
Visualization
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Summary:Modern humans are healthy because they use their communities to boost their self-esteem by learning to see themselves as contributing members of a larger whole. Our interactions with people are influenced by several things, including the way that people feel about themselves and the manner in kind of way people communicate it. In addition to reflecting self-awareness, the executive functions necessary for self-regulation are often associated with self-awareness. This study proposes a Deep Learning based Video Surveillance Framework (DLVSF) to track individuals and teach them to understand themselves better. A video surveillance system can keep tabs on people's whereabouts in real-time and identify and report any security holes in the system itself. Deep learning models educated on data gathered from real-time video surveillance allow for precise human physical activity classification. Thus, DL VSF efficiently enhances self-awareness, self-attention, and social management, as the numerical data shows. This paper's accuracy has increased, and the incident into a picture system allows behavior evaluation in a high-resolution area. Analyzing the pros and cons of utilizing visual monitoring devices to automatically detect suspicious activity using deep learning technologies and visual detection capabilities by DL VSF is accomplished.
DOI:10.1109/AICERA/ICIS59538.2023.10419999