A novel vision based Approach for instructor's performance and behavior analysis

Performance analysis of instructors in the lecture room plays a significant role in maintaining the higher education quality and standards. This paper presents a novel approach for the evaluation of instructor's performance and behavior in the lecture room. Proposed approach employs the lecture video using face recognition and pose estimation of instructor. Instructor time-in and time-out monitoring; walking, pointing, writing and addressing postures are focused for the analysis. Edge detection and texture based descriptor are suggested for the segmentation of lecture room scene; resulting in localization of white board and the presentation area. Gaussian mixture model and morphological operations are used for instructor detection during lecture. For time-in and time-out monitoring of instructor, face recognition is employed. For instructor pose estimation, morphological features of instructor's upper limb are extracted in the space-time. Space-time features of instructor's upper limb are classified into respective pose using Bayesian classification. Experimentation results shows 96% recognition rate for instructor selected postures. Proposed research work recommends an innovative enhancement to instructor performance analysis in the lecture room by generating a comprehensive activity analysis.