Action Recognition using Transfer Learning and Majority Voting for CSGO

TL;DR

This paper develops a transfer learning-based model with majority voting for real-time action recognition in CS:GO, addressing data access issues and pioneering video analysis in this game.

Contribution

It introduces a novel approach combining transfer learning and majority voting for CS:GO action recognition, filling a research gap in real-time game analysis.

Findings

Identified the best transfer learning model for CS:GO actions

Achieved high accuracy in real-time action prediction

Proposed a system to automate data collection and analysis

Abstract

Presently online video games have become a progressively favorite source of recreation and Counter Strike: Global Offensive (CS: GO) is one of the top-listed online first-person shooting games. Numerous competitive games are arranged every year by Esports. Nonetheless, (i) No study has been conducted on video analysis and action recognition of CS: GO game-play which can play a substantial role in the gaming industry for prediction model (ii) No work has been done on the real-time application on the actions and results of a CS: GO match (iii) Game data of a match is usually available in the HLTV as a CSV formatted file however it does not have open access and HLTV tends to prevent users from taking data. This manuscript aims to develop a model for accurate prediction of 4 different actions and compare the performance among the five different transfer learning models with our self-developed deep neural network and identify the best-fitted model and also including major voting later on, which is qualified to provide real time prediction and the result of this model aids to the construction of the automated system of gathering and processing more data alongside solving the issue of collecting data from HLTV.