Scene Classification in FPS Game Videos Based on Acoustic Information
Yusuke Maeda, Takahiro Hayashi · 2023
In this paper, we report on scene classification experiments in FPS (First-Person Shooter) game videos focusing solely on acoustic information. Generally, scene classification in videos considers both visual and acoustic information. However, the visual information in FPS games contains game-specific elements that make it challenging to construct scene classifiers with generalization capabilities. In contrast, acoustic information in FPS games tends to have more commonalities, such as weapon usage sounds, which can contribute to successful scene classification. Focusing on acoustic information that provides essential clues for scene identification in FPS games, in the experiments, we employed a simple CNN model to classify audio segments into combat and non-combat scenes. To evaluate the classification performance, we used an FPS game audio test set. The test set consists of audio segments extracted from the audio streams of FPS game videos. Each audio segment is assigned a scene label indicating whether it belongs to a combat or noncombat scene. The results show that game scenes can be roughly classified with a simple CNN model by focusing on just acoustic information of FPS games.