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Human pose estimation has become a crucial component of computer vision applications, powering innovations in fitness tracking, sports analytics, motion capture, and interactive AI systems. Among the many frameworks developed for this task, OpenPose vs MediaPipe often becomes the center of discussion due to their accessibility, flexibility, and strong community support. Both frameworks aim to extract human body keypoints from images or videos, but they differ significantly in architecture, performance, and implementation approach. This article explores the differences between OpenPose and MediaPipe, helping developers, researchers, and AI enthusiasts choose the right framework for their specific needs.   Understanding Human Pose Estimation Human pose estimation refers to the process of identifying and tracking key points of the human body—such as elbows, knees, or shoulders—in visual data. These key points are then connected to form a skeletal model that represents human...

  Artificial Intelligence (AI) has transformed nearly every industry by enabling machines to perform complex tasks autonomously. At the heart of this transformation lies a fundamental concept known as PEAS in AI , a framework used to describe and design intelligent agents. PEAS stands for Performance measure, Environment, Actuators, and Sensors . Understanding this model is essential for building efficient AI systems capable of perceiving their surroundings, making decisions, and taking appropriate actions. What Is PEAS in AI? PEAS in AI is a model that helps define the components required for an intelligent agent to function effectively. It provides a structured approach to describe how an agent perceives the world, processes information, and acts to achieve its goals. Each part of the acronym plays a crucial role: Performance Measure: Defines the success criteria or objectives that the AI agent strives to achieve. Environment: Represents the surroundings in ...