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People Counting in Classroom Based on Video Surveillance

机译：基于视频监控的教室人数统计

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摘要

Currently, the switches of the lights and other electronic devices in the classroom are mainly relied on manual control, as a result, many lights are on while no one or only few people in the classroom. It is important to change the current situation and control the electronic devices intelligently according to the number and the distribution of the students in the classroom, so as to reduce the considerable waste of electronic resources. This paper studies the problem of people counting in classroom based on video surveillance. As the camera in the classroom can not get the full shape contour information of bodies and the clear features information of faces, most of the classical algorithms such as the pedestrian detection method based on HOG (histograms of oriented gradient) feature and the face detection method based on machine learning are unable to obtain a satisfied result. A new kind of dual background updating model based on sparse and low-rank matrix decomposition is proposed in this paper, according to the fact that most of the students in the classroom are almost in stationary state and there are body movement occasionally. Firstly, combining the frame difference with the sparse and low-rank matrix decomposition to predict the moving areas, and updating the background model with different parameters according to the positional relationship between the pixels of current video frame and the predicted motion regions. Secondly, the regions of moving objects are determined based on the updated background using the background subtraction method. Finally, some operations including binarization, median filtering and morphology processing, connected component detection, etc. are performed on the regions acquired by the background subtraction, in order to induce the effects of the noise and obtain the number of people in the classroom. The experiment results show the validity of the algorithm of people counting.
机译：当前，教室中的照明灯和其他电子设备的开关主要依靠手动控制，结果，许多照明灯都亮着，而教室里没有人或只有很少的人。重要的是要根据教室中学生的人数和分布来改变现状并智能地控制电子设备，以减少可观的电子资源浪费。本文研究了基于视频监控的教室人数统计问题。由于教室中的摄像头无法获得人体的完整轮廓信息和面部的清晰特征信息，因此大多数经典算法，例如基于HOG（定向梯度直方图）特征的行人检测方法和面部检测方法基于机器学习的人无法获得满意的结果。鉴于教室中大多数学生几乎处于静止状态，偶尔会有身体运动，提出了一种基于稀疏和低秩矩阵分解的新型双重背景更新模型。首先，将帧差与稀疏和低秩矩阵分解相结合，以预测运动区域，并根据当前视频帧的像素与预测的运动区域之间的位置关系，用不同的参数更新背景模型。其次，使用背景减法基于更新后的背景确定运动对象的区域。最后，对通过背景减法获得的区域进行二值化，中值滤波和形态学处理，连通分量检测等操作，以引起噪声的影响并获得教室中的人数。实验结果证明了该算法的有效性。

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