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Vision Play - Using Advance Artificial Intelligence and Machine Learning Algorithms

Year: 2025
Type: article
Abstract: Vision Play" is an advanced AI-driven video analysis system based on artificial intelligence, machine learning, andcomputer vision that is aimed at redefining traditional visual data processing. Initially designed to support football analysis, theplatform has grown to be a flexible and scalable architecture used in sports, surveillance, and real-time monitoring applications.The system supports current models like YOLOv5 for real-time object recognition, optical flow for movement tracking, andKMeans clustering for team or object identification. Perspective transformation is applied to translate pixel-level information toreal-world coordinates, making possible precise speed, distance, and positioning measurement.The system handles video streamsto identify, categorize, and track objects such as players, referees, or pedestrians with accuracy, even for very dynamic or crowdedscenes. It produces relevant visualizations such as movement traces, heatmaps, and performance dashboards to enable users togain profound insights into behavior trends and spatial dynamics. Built in modularity and real-time capacity, "Vision Play" canhandle varied camera feeds and is extensible to cloud or edge infrastructures.Through automated processing of advanced videoanalysis operations, the system lowers human labor by a large margin and increases accuracy, uniformity, and decision-makingefficiency. Its multi-industry suitability makes it a desirable asset for analysts, strategists, security organizations, and researchersseeking to leverage smart video insights for performance optimization, security enhancement, and data-driven operation
DOI: 10.5281/zenodo.16444483
Authors: Dhankar, Usha, Rai, Aditya Prakash, Maheshwari, Harsh, Uppal, Deepanjal, Singhal, Ankit
Language: English
Open Access status: hybrid
OpenAlex ID: W6912376118
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