V3Trans-Crowd: A Video-based Visual Transformer for Crowd Management Monitoring

Autonomously monitoring and analyzing the behavior of the crowd is an open research topic in the transportation field. The real-time identification, tracking, and prediction of the crowd behavior is primordial to ensure smooth crowd management operations in many public areas such as public transport stations and streets. First, the complexity brought by the interaction and fusion from individual to group that needs to be assessed and analyzed. Second, the classification of these actions which might be useful in identifying danger and avoiding any undesired consequences. In this paper, we propose a transformer-based crowd management monitoring framework called V3Trans-Crowd that captures information from video data and extracts meaningful output to categorize the behavior of the crowd. We provide an improved hierarchical transformer for multi-modal tasks. Inspired by 3D visual transformer, our proposed 3D visual model, V3Trans-Crowd, has been shown to achieve great performances in terms of accuracy compared to state-of-the-art methods, all tested on the standard Crowd-11 dataset.