Action Recognition using Transfer Learning and Majority Voting for CSGO
This work addresses a domain-specific problem for the gaming industry by enabling automated action recognition in CS:GO, though it is incremental as it applies existing methods to a new dataset.
The authors tackled the lack of video analysis and action recognition in CS:GO gameplay by developing a model to predict four different actions using transfer learning and majority voting, achieving real-time prediction capabilities to automate data gathering and processing.
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.