ClassRoom-Crowd: A Comprehensive Dataset for Classroom Crowd Counting and Cross-Domain Baseline Analysis - Wenqian Jiang; Xiaohua Huang; Qun Zhao; Sheng Liu (Engineering Proceedings)
In recent years, with the rise of smart education, crowd counting technology has garnered increasing attention for its applications in educational environments. During epidemic outbreaks, ensuring the accurate monitoring of student density in classrooms and other public educational spaces has become crucial. Additionally, in day-to-day educational management, tasks such as optimizing the allocation of teaching resources and planning educational spaces heavily depend on precise crowd counting techniques. Current methods predominantly employ convolutional neural networks, which require large datasets for training. However, there is a lack of specialized datasets tailored to specific educational scenarios and extreme conditions. Furthermore, to enhance crowd movement predictions in real-world applications, these methods must demonstrate temporal coherence. To address these gaps, this paper introduces the ClassRoom-Crowd dataset, specifically designed for crowd counting in educational settings with extreme conditions and temporal continuity. The dataset consists of 7571 images with 172,898 annotated objects. Additionally, baseline results using state-of-the-art crowd counting algorithms under a cross-condition protocol are provided. The experimental findings reveal that domain shifts significantly impact model performance, underscoring the need for domain adaptation methods in crowd counting research, particularly within the smart education context.