Abstract
Human activity recognition (HAR) targets the methodologies to recognize the different actions from a sequence of observations. Vision-based activity recognition is among the most popular unobtrusive technique for activity recognition. Caring for the elderly who are living alone from a remote location is one of the biggest challenges of modern human society and is an area of active research. The usage of smart homes with an increasing number of cameras in our daily environment provides the platform to use that technology for activity recognition also. The omnidirectional cameras can be utilized for fall detection activity which minimizes the requirement of multiple cameras for fall detection in an indoor living scenario. Consequently, two vision-based solutions have been proposed: one using convolutional neural networks in 3D-mode and another using a hybrid approach by combining convolutional neural networks and long short-term memory networks using 360-degree videos for human fall detection. An omnidirectional video dataset has been generated by recording a set of activities performed by different people as no such 360-degree video dataset is available in the public domain for human activity recognition. Both, the models provide fall detection accuracy of more than 90% for omnidirectional videos and can be used for developing a fall detection system for indoor health care.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alwan, M., Rajendran, P.J., Kell, S., Mack, D., Dalal, S., Wolfe, M., Felder, R.: A smart and passive floor-vibration based fall detector for elderly. In: IEEE International Conference on Information & Communication Technologies (ICITA), pp. 1003–1007 (2006)
Estudillo-Valderrama, M.A., Roa, L.M., Reina-Tosina, J., Naranjo-Hernandez, D.: Design and implementation of a distributed fall detection system—personal server. IEEE Trans. Inf Technol. Biomed. 13, 874–881 (2009)
Kang, J.M., Yoo, T., Kim, H.C.: A wrist-worn integrated health monitoring instrument with a tele-reporting device for telemedicine and telecare. IEEE Trans. Instrum. Meas. 55, 1655–1661 (2006)
Lee, T., Mihailidis, A.: An intelligent emergency response system: preliminary development and testing of automated fall detection. J. Telemed. Telecare 11(4), 194–198 (2005)
Rougier, C., Meunier, J., St-Arnaud, A., Rousseau, J.: Robust video surveillance for fall detection based on human shape deformation. IEEE Trans. Circuits Syst. Video Technol. 21(5), 611–622 (2011)
Kwolek, B., Kepski, M.: Human fall detection on the embedded platform using depth maps and wireless accelerometer. Comput. Methods Programs Biomed. 117(3), 489–501 (2014)
Foroughi, H., Rezvanian A., Paziraee A.: Robust fall detection using human shape a multi-class support vector machine. In: IEEE 6th Indian conference on Computer Vision, Graphics & Image Processing (ICVGIP), pp. 413–420 (2008)
Foroughi, H., Yazdi, H.S., Pourreza, H., Javidi, M.: An eigenspace-based approach for human fall detection using integrated time motion image and multi-class support vector machine. In: IEEE 4th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 83–90 (2008)
Foroughi, H., Aski, B.S., Pourreza, H.: Intelligent video surveillance for monitoring fall detection of elderly in home environments. In: IEEE 11th International Conference on Computer and Information Technology (ICCIT), pp. 24–27 (2008)
Foroughi, H., Naseri, A., Saberi, A., Yazdi, H.S.: An eigenspace-based approach for human fall detection using integrated time motion image and neural network. In: IEEE 9th International Conference on Signal Processing (ICSP), pp. 1499–1503 (2008)
Miaou, S.-G., Sung, P.-H., Huang, C.-Y.: A customized human fall detection system using omni-camera images and personal information. In: Proceedings of the 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, D2H2 2006, pp. 39–42, USA, April (2006)
Charfi, I., Miteran, J., Dubois, J., Atri, M., Tourki, R.: Definition and performance evaluation of a robust SVM based fall detection solution. In: Proceedings of the 8th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2012, pp. 218–224, Italy, November 2012
Mastorakis, G., Makris, D.: Fall detection system using Kinect’s infrared sensor. J. Real-Time Image Proc. 9(4), 635–646 (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Li, Z., Gavrilyuk, K., Gavves, E., Jain, M., Snoek, C.G.M.: Video LSTM convolves, attends and flows for action recognition. Comput. Vis. Image Underst. 166, 41–50 (2018)
Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35, 221–231 (2013)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei, L.F.: Large-scale video classification with convolutional neural networks. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1725–1732, Columbus, OH, USA, 23–28 June 2014
Sharma, S., Kiros, R., Salakhutdinov, R.: Action recognition using visual attention (2015). arXiv:1511.04119
Ullah, A., Ahmad, J., Muhammad, K., Sajjad, M., Baik, S.W.: Action recognition in video sequences using deep Bi-directional LSTM with CNN features. IEEE Access. 6, 1155–1166 (2018)
Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Dar-rell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2625–2634, Boston, MA, USA, 8–10 June 2015
Shi, Y., Tian, Y., Wang, Y., Huang, T.: Sequential deep trajectory descriptor for action recognition with three-stream CNN. IEEE Trans. Multimedia 19, 1510–1520 (2017)
Ng, J.Y., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: deep networks for video classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4694–4702, Boston, MA, USA, 8–10 June 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dhiraj et al. (2020). Activity Recognition for Indoor Fall Detection in 360-Degree Videos Using Deep Learning Techniques. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_33
Download citation
DOI: https://doi.org/10.1007/978-981-32-9291-8_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-32-9290-1
Online ISBN: 978-981-32-9291-8
eBook Packages: EngineeringEngineering (R0)