An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification

In this paper, we present the task of audio-visual scene classification (SC) where input videos are classified into one of five real-life crowded scenes: ‘Riot’, ‘Noise-Street’, ‘Firework-Event’, ‘Music-Event’, and ‘Sport-Atmosphere’. To this end, we firstly collect an audio-visual dataset (videos) of these five crowded contexts from Youtube (in-the-wild scenes). Then, a wide range of deep learning classification models are proposed to train either audio or visual input data independently. Finally, results obtained from high-performance models are fused to achieve the best accuracy score. Our experimental results indicate that audio and visual input factors independently contribute to the SC task’s performance. Notably, an ensemble of deep learning models can achieve the best accuracy of 95.7%.