The Research And Design Of The Classroom Statistics System Based On Video Surveillance

In recent years,with the continuous rise of social economy,video surveillance equipment has been popular,and a large number of video surveillance systems have been widely used in the station,shopping malls,scenic spots,roads and other public places.The increasing improvement of image processing technology provides a technical support for the intelligence of video surveillance system.Classrooms are the main places for students to study,but students often spend a large amount of time finding a suitable learning room because of their class time or because some other students take up the classrooms,so it is an extremely valuable and significant research topic how we manage available classrooms efficiently,make good use of them and help students find them quickly and effectively.As cameras in the classroom are installed above the classroom obliquely,and with students sitting on the seats,tables and chairs shelter their body from cameras,cameras can not get a complete outline of the human body information and clear facial features.A human head has become a breakthrough to solve the demographic problem.This paper makes a new exploration and research on the head detection in the video surveillance system under the classroom scene by using the fixed features of the head contour.It focuses on the human head detection method in classroom scene video,and uses the training head classifier to detect and identify the target.The main contents of the paper are as follows:1.This paper will make an introduction to the principle of the image preprocessing method and the target detection method and implement it.It uses bilateral filtering to remove noise to make the edge of the image information more complete,and to ensure good quality for the follow-ups.Besides,it designs a demographic method based on classroom video surveillance by the comparative analysis of some of the more mature statistics methods and for the classroom-specific environment.2.Inspired by the successful application of SVM algorithm in pedestrian detection,this paper presents a statistical method based on human head detection.This method combines the improved Adaboost algorithm and the support vector machine SVM algorithm.First,the Adaboost algorithm is used to train the haar head detector to detect the candidate area in the original image.In order to reduce the false detection rate as much as possible,the improved Adaboost algorithm is used to further divide the misclassified samples during the training process.In addition,in order to improve the detection speed,we here use cascaded strong classifier algorithm to construct the classifier,and the SVM algorithm is used to train the HOG head classifier,and then the second candidate is selected for the first candidate area,which effectively removes the large number of false alarms and improves the accuracy of the head test results.3.In order to further improve the accuracy of the results of statistic,the head size check method and seat verification method are used to amend the number of its statistical results.At the same time,additional empty classroom photos,are compared with the pictures in the classroom where students have class.Further accuracy of the result is made through analysis of the difference between students in the classrooms and empty classrooms,and the system has a good practical value.The results of the final experiment show that the number of statistical methods proposed in this paper can be applied to the classroom scene,which can accurately detect the number of people in the camera range in real time and in the classroom scene under the premise of guaranteeing the high accuracy rate.