Problems and possibilities of using video analytics in the fields of education and entertainment
DOI:
https://doi.org/10.25726/d6189-0390-2258-xKeywords:
video analytics, emotions, recognition, educationAbstract
Rapid changes and improvements in technology have revolutionized the modern world. Humancomputer Interaction (HCI) has evolved over a period of time, transforming many aspects of our lives, including how we learn. Currently, students can benefit from the rapid exchange of information, accessibility on the Internet, and practical implementation of what was previously taught only in books. Learning experience and competence depend on how well the subject is taught to students and through what medium. Books and text resources have proven to be a great delivery method over time and have been used for centuries. Audio and video materials have also proven to be an effective way to deliver information, as they provide a good amount of rich content in a relatively short period, which has led to increased motivation of students in the classroom and a change in the perception of teachers. However, the lack of immersion and control makes video-based learning less personal than interactive classes and real-life simulations. Video surveillance using video analytics can be deployed to monitor territories at certain times of the day. For example, once a school opens, there shouldn't be a lot of activity in the parking lot or in certain places around the school. In such situations, smart cameras with video analytics can be used to detect activity in areas of interest, to warn the school security service that something may require their attention. Radar detection is ideal for perimeters, where the device can be unobtrusively configured to alert when someone enters a certain area.
References
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al. (2016). “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (Savannah, GA: USENIX Association), 265–283.
Chi, Y. M., Wang, Y.-T., Wang, Y., Maier, C., Jung, T.-P., and Cauwenbe, G. (2012). Dry and noncontact EEG sensors for mobile brain–computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 228–235. doi: 10.1109/TNSRE.2011.2174652
Danelljan, M., Robinson, A., Khan, F. S., and Felsberg, M. (2016). “Beyond correlation filters: learning continuous convolution operators for visual tracking,” in European Conference on Computer Vision, eds B. Leibe, J. Matas, N. Sebe, and M. Welling (Amsterdam: Springer), 472–488. doi: 10.1007/978-3-319-46454-1_29
Domínguez-Jiménez JA, Campo-Landines KC, Martínez-Santos JC, Delahoz EJ, ContrerasOrtiz SH. A machine learning model for emotion recognition from physiological signals. Biomedical Signal Processing and Control. 2020;55:101646.
Fu, Q., Luo, Y., Liu, J., Bi, J., Qiu, S., Cao, Y., et al. (2017). “Improving learning algorithm performance for spiking neural networks,” in 2017 IEEE 17th International Conference on Communication Technology (ICCT) (Chengdu: IEEE), 1916–1919. doi: 10.1109/ICCT.2017.8359963
Gavrilescu, M., (2015) “Recognizing emotions from videos by studying facial expressions, body postures and hand gestures”, 23rdTelecommunication fourm TELFOR, pp. 720-723.
Gu Y, Wang Y, Liu T, Ji Y, Liu Z, Li P, et al. EmoSense: Computational Intelligence Driven Emotion Sensing via Wireless Channel Data. IEEE Transactions on Emerging Topics in Computational Intelligence. 2020;4(3):216–226.
Heike Brock. (2018) “Deep learning - Accelerating Next Generation Performance Analysis Systems” 12th Conference of the International Sports Engineering Association, Brisbane, Queensland, Australia, pp. 26–29.
Hossain MS, Muhammad G. Emotion-Aware Connected Healthcare Big Data Towards 5G. IEEE Internet of Things Journal. 2018;5(4):2399–2406.
Liu, J., Huang, Y., Luo, Y., Harkin, J., and McDaid, L. (2019). Bio-inspired fault detection circuits based on synapse and spiking neuron models. Nerocomputing 331, 473–482. doi: 10.1016/j.neucom.2018.11.078
Munoz MO, Foster R, Hao Y. Exploring Physiological Parameters in Dynamic WBAN Channels. IEEE Transactions on Antennas and Propagation. 2014;62(10):5268–5281.
Niewiadomski Mancini, Varni Volpe, Camurri Automated Laughter detection from full body movements” IEEE Transactions on Human-Machine Systems, 46 (1) (2016), pp. 113-213
Schwartz G, Tee BCK, Mei J, Appleton AL, Kim DH, Wang H, et al. Flexible polymer transistors with high pressure sensitivity for application in electronic skin and health monitoring. Nature communications. 2013;4:1859. pmid:23673644
Shirbhate Neha, Talele Kiran, (2016), “Human Body Language Understanding for Action detection using Geometric Features”, 2ndInternational Conference on Contemporary Computing and Informatics, IEEE, pp.603-607.
Soroush, M. Z., Maghooli, K., Setarehdan, S. K., and Nasrabadi, A. M. (2019). A novel EEGbased approach to classify emotions through phase space dynamics. Signal Image Video Process. 13, 1149–1156. doi: 10.1007/s11760-019-01455-y
Wang X, Le D, Cheng H, Xie C. All-IP wireless sensor networks for real-time patient monitoring. Journal of biomedical informatics. 2014;52:406–417.
Wang, X., Zhang, T., Xu, X., Chen, L., Xing, X., and Chen, C. L. P. (2018). “EEG emotion recognition using dynamical graph convolutional neural networks and broad learning system,” in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (Madrid: IEEE), 1240–1244. doi: 10.1109/BIBM.2018.8621147
