Multi-modality Based Affective Video Summarization for Game Players

Games has been considered as a benchmark for practicing computational models to analyze players interest as well as its involvement in the game. Though several aspects of game related research are carried out in different fields of research including development of game contents, avatar's control in games, artificial intelligent competitions, analysis of games using professional gamer's feedback, and advancements in different traditional and deep learning based computational models. However, affective video summarization of gamer's behavior and experience are also important to develop innovative features, in-game attractions, synthesizing experience and player's engagement in the game. Since it is difficult to review huge number of videos of experienced players for the affective analysis, this study is designed to generate video summarization for game players using multi-modal data analysis. Bedside's physiological and peripheral data analysis, summary of recorded videos of gamers is also generated using attention model-based framework. The analysis of the results has shown effective performance of proposed method.