  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.
