Recognition of Student Classroom Behaviors Based on Moving Target Detection

Abstract

Classroom teaching, as the basic form of teaching, provides students with an important channel to acquire information and skills. The academic performance of students can be evaluated and predicted objectively based on the data on their classroom behaviors. Considering the complexity of classroom environment, this paper firstly envisages a moving target detection algorithm for student behavior recognition in class. Based on region of interest (ROI) and face tracking, the authors proposed two algorithms to recognize the standing behavior of students in class. Moreover, a recognition algorithm was developed for hand raising in class based on skin color detection. Through experiments, the proposed algorithms were proved as effective in recognition of student classroom behaviors.

Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

“…Precision, recall, and F1 score were used as the evaluation indexes to verify the accuracy of MOD-AT, and their calculation formulas are ( 21), (22), and (23), respectively. The test data for four moving object detection algorithms are recorded every 30 frames.…”

“…Moving object detection methods based on traditional single threshold These methods mainly include the frame difference [16][17][18], optical flow [19,20], and background difference methods [21][22][23], among others. For example, Zuo et al [24] improved the accuracy of moving object detection based on the background frame difference method.…”

Influence of Spatiotemporal Difference and Input of Higher Education on Regional Economy

“…e equilibrium effect of the spatiotemporal difference of higher education development on regional economic growth was equivalent to the effect of H Q on � a. Formula (15) shows that � a is a power function of H Q . en, we have…”

“…e spatiotemporal difference of higher education is usually analyzed through literature review, comparative analysis, regression analysis, statistical analysis, and projection tracking [11][12][13][14][15][16]. Rokicki et al [17] explored the causes for imbalanced spatiotemporal distribution of higher education resources on two scales (time and space) and in three dimensions (scale, faculty, and fund investment) and obtained the global and local autocorrelations of the distributions for multiple influencing factors: regional difference in population density, regional difference in economic development, and regional difference in fund investment.…”

“…Equilibrium Law Analysis. Formula(15) indicates that the balanced development of regional economy is jointly affected by three groups of factors: (1) the asymmetric effect of the four coupling mechanisms H SI , H E , H C , and H Q ; (2) the effect of three growth rate factors h E , h A , and h C ; and (3) the effect of correlation coefficients of economic structure, capital discount, and consumption β, ξ, δ, and ω.…”