Human Motion Detection: A Review of Techniques and Applications
Keywords:
Artificial Intelligence (AI), Gaussian Mixture Models (GMM), Human Motion Detection System (HMDS), Internet of Things (IoT), Support Vector Machines (SVM)Abstract
A Human Motion Detection System (HMDS) is a cutting-edge technology designed to detect, track and analyze human movement within a specified environment. HMDS enables accurate identification of human motion patterns.
Human motion detection is an essential component of security, surveillance, healthcare, and smart systems. This paper provides an extensive review of various methodologies used in motion detection, including background subtraction, optical flow, and deep learning-based techniques. Each method's strengths and weaknesses are analyzed to determine its suitability for real-world applications. With the advent of Artificial Intelligence (AI) and the Internet of Things (IoT), motion detection technologies are becoming more precise and efficient. This review offers insights into current trends and potential future developments in this field. Human motion detection is a rapidly evolving field with applications across diverse domains, including surveillance, healthcare, human-computer interaction, and augmented reality. Various techniques have been developed over the years, ranging from traditional computer vision methods to advanced machine learning models suitability for different environments. The paper also discusses the integration of motion detection with Internet of Things (IoT) devices and its impact on real-time applications. Additionally, it highlights challenges such as occlusion, dynamic backgrounds, and real-time processing limitations, while emphasizing the potential for future advancements. By providing a comprehensive overview of existing methodologies and emerging trends, this review aims to assist researchers and developers in selecting appropriate techniques for specific applications and encouraging further innovation in the field of human motion detection.
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