Deep Tracking Students Identity and Motion in Classroom Our focus here is tracking on identity and head pose of students for helping educational teaching in classroom. By using computer vision technology, we are able to extract identity information and head pose from all students in the video stream from the classroom in realtime. Firstly, merging multiscale feature map for solving the crowd face detection in classroom. Secondly, multiple face recognition and tracking in the classroom, based on the database of studentu0027s multiview facial images. Finally, face landmark detection based on 68 points was improved with neural network. After calculating the threedimensional angle of the head pose, we attempted to analyze the correlation with the four motion of students by observing time series of the three angle changes, and configured the hybrid model and the training support vector machines (SVM) classifier, to achieve the correct rate of 85.86%.

Face detection and tracking using edge orientation information An important topic in face recognition as well as in video coding or multimodal human machine interfaces is the automatic detection of faces or headandshoulder regions in visual scenes. The algorithms therefore should be computationally fast enough to allow an online detection and parallel processing of the detected objects. In this paper we describe our ongoing work on face detection using an approach that models the face appearance by edge orientation information. We will show that edge orientation is a powerful local image feature to describe objects like faces or body parts. We will present a simple and efficient method for template matching and object modeling based solely on edge orientation information. We also show how to obtain an optimal face model in the edge orientation domain from a set of training images. Unlike many approaches that model the grey level appearance of the face our approach is computationally very fast. It takes less than 0.1 seconds on a Pentium II 500MHz for a 190x140 image to be processed using a multiresolution search with three resolution levels. We demonstrate the capability of our detection method on an image database of 7000 images taken from more than 200 different people. The variations in head size, lighting and background are considerable. The obtained detection rate is more than 97% on that database. We have also extended the algorithm for face tracking. The tracking capabilities are shown using results from a real  time implementation of the proposed algorithm.