Architectural Optimization of Computer Vision Surveillance
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Date
2024-06-30
Authors
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UNIVERSITY MOHAMED BOUDIAF MSILA, FACULTY OF MATHEMATHICS AND COMPUTER SCIENCE, DEPARTEMENT OF COMPUTER SCIENCE
Abstract
This thesis presents a Robust Framework featuring two algorithms MPSTA
and MARCPR, an innovative approach to optimizing automated human surveillance systems, which are often hindered by their inherent complexity and the
rapid evolution of computer vision techniques. We introduce a human-behavior
profiling system that addresses constraints architecturally, eliminating the need
for synchronism. Our system outperforms continuous detection systems in
terms of speed, it tackles each detection challenge using various computer vision techniques, thereby enhancing its efficiency and effectiveness. We propose a novel human behavior profiling approach, a system featuring a hybrid computer-vision kernel with dual execution times distributed architecture along with pattern extraction/employment, pre-processing techniques,
and feature engineering. These methods have the potential to enhance profiling performance and accuracy in certain contexts, enabling the detection and
analysis of both collective behaviors, such as interactions and conflicts among
individuals, and distinct individual behaviors.
Description
Keywords
Human Behavior Profiling System, Surveillance Systems, Computer Vision Techniques, Interconnected Constraints