CCSS: An Effective Object Detection System for Classroom Crowd Statistics

The crowd statistics technology has been widely applied to smart classroom, manual roll call and campus security in recent years. However, due to challenges like low resolution, shooting angels and partial overlapping of students in the classroom, it's extremely hard to estimate the number of students accurately. Inspired by the improvements of object detection models in image target classification and location, we implements a classroom crowd statistics system (CCSS) to provide statistical information on the number of students for the construction of the wisdom classroom. In addition, we introduce a new large-scale classroom dataset, which contains 3,070 images in the classroom environment and 106,304 student annotations. To the best of our knowledge, this is the first student counting dataset collected under the classroom settings, which will greatly promote the development of classroom crowd statistics based on deep learning. In order to further improve the accuracy and speed of students detecting, we also modify the YOLOv4 algorithm to make it more adaptive for this task. The experimental results show that our model gains a significant improvement over the selected baselines on the proposed dataset.