Revolutionizing Visual Perception: The Role of Artificial Intelligence in Computer Vision

Abstract

This paper systematically reviews intelligent tutoring systems (ITS) that utilize gross body movement detection through computer vision. By analysing recent advancements, methodologies, and applications, we identify key trends and challenges in leveraging movement data to enhance personalized learning experiences. The study highlights how computer vision techniques enable real-time monitoring and adaptive feedback, promoting active and engaging educational environments. The integration of artificial intelligence and computer vision in healthcare has revolutionized diagnostics, patient monitoring, and surgical assistance. This review explores state-of-the-art techniques and applications, emphasizing accuracy, real-time processing, and ethical considerations. Key challenges such as data privacy and interpretability are discussed, along with prospects for enhancing healthcare outcomes through AI-driven vision systems. This comprehensive review traces the evolution of computer vision and pattern recognition techniques used for left ventricle segmentation over the past five decades. By examining methodological progress and clinical applications, the study highlights major breakthroughs and ongoing challenges, paving the way for improved cardiac imaging and diagnostic accuracy. Sensor planning plays a pivotal role in optimizing computer vision systems by strategically positioning sensors for maximal data acquisition. This survey covers fundamental concepts, recent advancements, and practical implementations, offering insights into multi-sensor coordination and adaptive strategies for enhanced visual perception. The construction industry is increasingly adopting computer vision technologies to enhance safety by monitoring hazardous zones, detecting safety violations, and analysing worker movements. This paper explores the latest applications and how AI-driven systems are reducing workplace accidents and improving compliance with safety protocols.

Introduction

A. Definition

AI in Computer Vision refers to using AI techniques—like machine learning and deep learning—to enable machines to interpret and analyze visual data (images, videos, real-time feeds). These systems can recognize objects, patterns, and make decisions more accurately than traditional methods.

B. Need for AI in Computer Vision

• Automates complex tasks (e.g., image classification, object detection).

• Enables scalable and real-time analysis in fields like:

  • Facial recognition

  • Medical imaging

  • Smart surveillance

• Overcomes the limitations of rule-based vision systems.

• Powers applications such as gesture recognition, autonomous vehicles, and intelligent interaction systems.

C. Importance

• Deep learning provides high accuracy in image analysis.

• Enables real-time processing of large visual datasets.

• Used in diverse areas: robotics, healthcare, AR, security, and construction.

• Improves safety, reduces human error, and automates labor-intensive tasks.

• AI models improve with more data and adapt to dynamic environments.

D. Key Aspects of AI in Computer Vision

???? II. Literature Review – Research Developments

AI in computer vision has driven innovations across many fields:

Key Applications:

• Healthcare: Improved diagnostics, left ventricle segmentation with deep learning.

• Education: ITS (Intelligent Tutoring Systems) using gesture/movement detection.

• Construction Safety: Hazard detection and site monitoring.

• Urban Planning: AI helps analyze city infrastructure and support smart cities.

• Robotics: AI-driven vision improves robotic tasks like welding precision.

• Deployment Efficiency: Research into edge computing, containerization, and sparse representation allows CV systems to work on low-resource devices.

Common Themes:

• Expansion into real-time, scalable, and intelligent systems.

• Use of hybrid AI methods (e.g., CNNs + RNNs) for improved performance.

• Ongoing improvements in accuracy, robustness, and real-world adaptability.

???? III. Comparison of Five Key Research Papers

Conclusion

The research papers highlight the rapid advancements in AI-driven computer vision across various domains, including education, healthcare, medical imaging, sensor planning, construction safety, and robotics. Innovations like Intelligent Tutoring Systems, deep learning models, sensor optimization, and machine vision are enhancing diagnostics, safety, and personalized learning. Moreover, research in edge device optimization, sparse representation, and hybrid learning algorithms continues to refine AI\'s efficiency and applicability. The integration of AI in computer vision remains a transformative force, promising enhanced performance, precision, and adaptability across diverse industries.

References

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Copyright

Copyright © 2025 Khan Subham, Maleeha Fatima, Dr. Goldi Soni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.