An Audio-Visual Dataset and Deep Learning Frameworks for Crowded Scene Classification
This work addresses scene classification for crowded environments, but it is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled the problem of classifying videos into five real-life crowded scenes using audio-visual data, achieving a best accuracy of 95.7% through an ensemble of deep learning frameworks.
This paper presents a 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 frameworks are proposed to deploy either audio or visual input data independently. Finally, results obtained from high-performed deep learning frameworks 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. Significantly, an ensemble of deep learning frameworks exploring either audio or visual input data can achieve the best accuracy of 95.7%.