Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Multimodal behavior analysis in computer-enabled laboratories using nonverbal cues(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Banerjee, S.; Ashwin, T.S.; Guddeti, R.M.R.In the modern era, there is a growing need for surveillance to ensure the safety and security of the people. Real-time object detection is crucial for many applications such as traffic monitoring, security, search and rescue, vehicle counting, and classroom monitoring. Computer-enabled laboratories are generally equipped with video surveillance cameras in the smart campus. But, from the existing literature, it is observed that the use of video surveillance data obtained from smart campus for any unobtrusive behavioral analysis is seldom performed. Though there are several works on the students’ and teachers’ behavior recognition from devices such as Kinect and handy cameras, there exists no such work which extracts the video surveillance data and predicts the behavioral patterns of both the students and the teachers in real time. Hence, in this study, we unobtrusively analyze the students’ and teachers’ behavioral patterns inside a teaching laboratory (which is considered as an indoor scenario of a smart campus). Here, we propose a deep convolution network architecture to classify and recognize an object in the indoor scenario, i.e., the teaching laboratory environment of the smart campus with modified Single-Shot MultiBox Detector approach. We used six different class labels for predicting the behavioral patterns of both the students and the teachers. We created our dataset with six different class labels for training deep learning architecture. The performance evaluation demonstrates that the proposed method performs better with an accuracy of 0.765 for classification and localization. © 2020, Springer-Verlag London Ltd., part of Springer Nature.Item Surveillance video analysis for student action recognition and localization inside computer laboratories of a smart campus(Springer, 2021) Rashmi, M.; Ashwin, T.S.; Guddeti, G.R.M.In the era of smart campus, unobtrusive methods for students’ monitoring is a challenging task. The monitoring system must have the ability to recognize and detect the actions performed by the students. Recently many deep neural network based approaches have been proposed to automate Human Action Recognition (HAR) in different domains, but these are not explored in learning environments. HAR can be used in classrooms, laboratories, and libraries to make the teaching-learning process more effective. To make the learning process more effective in computer laboratories, in this study, we proposed a system for recognition and localization of student actions from still images extracted from (Closed Circuit Television) CCTV videos. The proposed method uses (You Only Look Once) YOLOv3, state-of-the-art real-time object detection technology, for localization, recognition of students’ actions. Further, the image template matching method is used to decrease the number of image frames and thus processing the video quickly. As actions performed by the humans are domain specific and since no standard dataset is available for students’ action recognition in smart computer laboratories, thus we created the STUDENT ACTION dataset using the image frames obtained from the CCTV cameras placed in the computer laboratory of a university campus. The proposed method recognizes various actions performed by students in different locations within an image frame. It shows excellent performance in identifying the actions with more samples compared to actions with fewer samples. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.