AVMSN: An Audio-Visual Two Stream Crowd Counting Framework Under Low-Quality Conditions

Abstract

Crowd counting is considered as the essential computer vision application that uses the convolutional neural network to model the crowd density as the regression task. However, the vision-based models are hard to extract the feature under low-quality conditions. As we know, visual and audio are used widely as media platforms for human beings to touch the physical change of the world. The cross-modal information gives us an alternative method of solving the crowd counting task. In this case, in order to solve this problem, a model named the Audio-Visual Multi-Scale Network (AVMSN) is established to model the unconstrained visual and audio sources for completing the crowd counting task in this paper. Based on the Feature extraction and Multi-modal fusion module, in order to handle the objects of various sizes in the crowd scene, the Sample Convolutional Blocks are adopted by the AVMSN as the multi-scale Vision-end branch in the Feature extraction module to calculate the weighted-visual feature. Besides, the audio, which is the temporal domain transformed into the spectrogram information and the audio feature is learned by the audio-VGG network. Finally, the weighted-visual and audio features are fused by the Multi-modal fusion module, which adopts the cascade fusion architecture to calculate the estimated density map. The experimental results show the proposed AVMSN achieves a lower mean absolute error than other state-of-art crowd counting models under the low-quality conditions.