An Hierarchical Framework for Classroom Events Classification
In this paper, a model for classroom events classification is proposed. Major classroom events which are considered in this work are drowsiness of a student, group discussion, steady and alert, and noisy classroom. These events are classified using a two level classification model. It makes use of simple threshold based classifiers for classification. In the first level, classes such as noisy classroom and drowsiness are separated from that of remaining classes based on global threshold. The global threshold is computed based on correlation coefficients, computed across the intensity values of the video frames. The correlation scores obtained from each video are used for classification. During second level, a partially labeled video is classified as a member of any of the said four classes based on the local threshold computed from each class of videos. Local threshold is computed based on the global characteristics extracted from the videos. For classification purpose, the events which are considered here are strictly mutually exclusive events. Due to the lack of classroom events video datasets, the dataset has been created consisting of 96 videos spread across 4 different classes. The proposed model is validated using suitable validity measures viz., accuracy, precision, recall, and f-measure. The results show that the proposed model performs better in classifying the said events.
KeywordsClassroom events Statistical features Threshold based classifier
The authors would like to acknowledge the support rendered by IISc Bangalore for providing the VADS resources. The second author would also acknowledge the Department of Science & Technology, INDIA, for their financial support through DST-INSPIRE fellowship.
- 1.Zhang, L., Li, S.Z., Yuan, X., Xiang, S.: Real-time object classification in video surveillance based on appearance learning. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)Google Scholar
- 2.Guru, D.S., Manjunath, S., Kiranagi, B.B.: SVARS: Symbolic video archival and retrieval system. In: Bangalore Compute Conference, vol. 4, pp. 1–9 (2010)Google Scholar
- 4.Guru, D.S., Dallalzadeh, E., Manjunath, S.: A symbolic approach for classification of moving vehicles in traffic videos. ICPRAM 2, 351–356 (2012)Google Scholar
- 6.Kotikalapudi, U.K.: Abnormal event detection in video, M. Tech Thesis. Indian Institute of Science, Bangalore (2007)Google Scholar
- 7.Cui, L., Li, K., Chen, J., Li, Z.: Abnormal event detection in traffic video surveillance based on local features. In: Image and Signal Processing (CISP), pp. 362–366 (2011)Google Scholar
- 8.Zhong, H., Shi, J., Visontai, M.: Detecting unusual activity in video. In: Computer Vision and Pattern Recognition, pp. II-819-II-826 (2004)Google Scholar
- 10.Fleischman, M., Roy, D.: Temporal feature induction for baseball highlight classification. In: Proceedings of ACM Multimedia, pp. 333–336 (2007)Google Scholar
- 11.Harikrishna, N., Sanjeev, S., Sriram, D.S.: Automatic summarization of cricket video events using genetic algorithm. In: Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, Oregon, USA, pp. 2051–2054 (2010)Google Scholar