Due to the high demand of deep learning for data quantity, semi-supervised learning (SSL) has a very important application prospect because of its successful use of unlabeled data. Existing SSL algorithms have achieved high accuracy on MINIST, CIFAR-10 and SHVN datasets, and even outperform fully supervised algorithms. However, because the above three datasets have the characteristics of balanced data categories and simple identification tasks which can’t be ignored for classification problems, the SSL algorithm has uncertainties of effectiveness in the case of unbalanced datasets and specific recognition tasks. We analyze the datasets and find that the number of “disgust” in expressions dataset is less than other categories, and so is “discussion” in the classroom action recognition dataset. Therefore, we use a novel SSL model: Deep Co-Training (DCT) model to experiment on the expression recognition database (FER2013), as well as our own classroom student action database (BNU-LCSAD) and analyze the effectiveness of the algorithm in specific application scenarios. Moreover, we use a training strategy of TSA when train our model to solve the problem of being easily overfitting which is more likely to occur when data categories are not balanced. The experimental results prove the effectiveness of the SSL algorithm in practical application and the significance of using TSA.
With the development and application of digital cameras, especially in education, a great number of digital video recordings are produced in classrooms. Taking Beijing Normal University as an example, 3.4 TB of videos are recorded every day in more than 200 classrooms. Such huge data is beneficial for us, computer vision researchers, to automatically recognize students' classroom actions and even evaluate the quality of classroom teaching. To focus action recognition on students, we propose Beijing Normal University Large-scale Classroom Student Action Database version 1.0(BNU-LCSAD) which is the first large-scale classroom student action database for student action recognition and consists of 10 classroom student action classes from digital camera recordings at BNU. We introduce the construct and label Processing of this database in detail. In Addition , we provide baseline of student action recognition results based our new database using C3D network.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.