# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 1 Introduction
### Existing Systems  
There are several solutions for the proposed system such that the monitoring of students' behaviors can be achieved to evaluate their studying performances. Assessing the progress of learners has been explored in an environment without digitally quantified inputs and their uninformative possibilitieswere calculated for the implementation [12]. They developed a system that can monitor attention in the classroom during the lecture which can lead to two possible outcomes: a real-time reporting system or summary report. The main focus aspects are quantifying body motion and the estimation of eye-gaze direction. With eye-gaze direction, there can be three distinct directions: the teacher/slide, notebook/bench, and other directions. The motion metrics were tested by annotating the regions in which each student resides and measuring the amount of movement inside of it. The data was collected and ready for supervised machine-learning. However, in this paper, only assumptions and theories about the way to address students' behaviors are deeply investigated. A doctoral thesis for the program in computing and communication [13] showed the approaches to evaluate attention by metrics: motion, gaze estimation, and body-pose detection. For motion, differences in attention are manifested on the level of audience movement synchronization with the idea that attentive students would have a common behavior pattern. The relationship between head orientation and gaze direction was also studied. The combination of head detection and pose estimation was used to extract measures of audience head and gaze behavior. Meanwhile, the synchronization of student's head orientation and teacher's motion serves as a reliable indicator of the attentiveness of students. They showed that the behavior which can be used for the project is moving, but they needed to work around their assumption about the experiment. A data analysis module with the integration of computer vision technologies and machine learning algorithms to perform attendance taking was investigated to understand the students' behaviors and students' motion with minimum human intervention [14]. The computer vision system uses cameras placed in a suitable location in the classroom as its data collector module; facial recognition and body-motion detection are applied to take attendance and behavior analysis. Haar cascade face detection is applied to detect faces, and Eigenface and Fisherface approaches. These approaches are used to train and recognize students' faces. For body detection, the cascade classifier and histogram of oriented gradients (HOG) are used. There are four rules of body detection which are based on \"face is detected,\" \"upper body is detected,\" \"full-body is detected.\" Furthermore, they lead to performances: sitting and concentrating in classroom, sitting but not concentrating in classroom, and standing and ready to leave the classroom. Some specialized digital devices, such as Kinect from Microsoft, have been employed [15] to utilize the capabilities of collecting behavioral data of multiple students. The students' attention was evaluated by five human observers, who noted types of behavior from each student: writing, yawning, supporting head, leaning back, or gazing, and then found the attention level for each of the behavior; each behavior had a different range to evaluate the level of attention, and that was calculated by taking the mean of them. But there were some limitations: the ground truth data on attention, computed from human observer estimates, was not entirely reliable (need better evaluation of attention level); the training data was not large enough; and the Kinect sometimes detected incorrectly and produced erroneous results. In addition, the seven features computed from low-level Kinect data were not comprehensive enough to be able to describe all observed behavioral differences of the test persons (e.g., cannot detect writing).