Authors: Usha Dhankar, Aditya Prakash Rai, Harsh Maheshwari, Deepanjal Uppal, Ankit Singhal
Abstract: Vision Play" is an advanced AI-driven video analysis system based on artificial intelligence, machine learning, and computer vision that is aimed at redefining traditional visual data processing. Initially designed to support football analysis, the platform 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, and KMeans clustering for team or object identification. Perspective transformation is applied to translate pixel-level information to real-world coordinates, making possible precise speed, distance, and positioning measurement.The system handles video streams to identify, categorize, and track objects such as players, referees, or pedestrians with accuracy, even for very dynamic or crowded scenes. It produces relevant visualizations such as movement traces, heatmaps, and performance dashboards to enable users to gain profound insights into behavior trends and spatial dynamics. Built in modularity and real-time capacity, "Vision Play" can handle varied camera feeds and is extensible to cloud or edge infrastructures.Through automated processing of advanced video analysis operations, the system lowers human labor by a large margin and increases accuracy, uniformity, and decision-making efficiency. Its multi-industry suitability makes it a desirable asset for analysts, strategists, security organizations, and researchers seeking to leverage smart video insights for performance optimization, security enhancement, and data-driven operation.
DOI: https://doi.org/10.5281/zenodo.16444484
