Abstract
Managing distributed smart surveillance system is identified as a major challenging issue due to its comprehensive aggregation and analysis of video information on the cloud. In smart healthcare applications, remote patient and elderly people monitoring require a robust response and alarm alerts from surveillance systems within the available bandwidth. In order to make a robust video surveillance system, there is a need for fast response and fast data analytics among connected devices deployed in a real-time cloud environment. Therefore, the proposed research work introduces the Cloud-based Object Tracking and Behavior Identification System (COTBIS) that can incorporate the edge computing capability framework in the gateway level. It is an emerging research area of the Internet of Things (IoT) that can bring robustness and intelligence in distributed video surveillance systems by minimizing network bandwidth and response time between wireless cameras and cloud servers. Further improvements are made by incorporating background subtraction and deep convolution neural network algorithms on moving objects to detect and classify abnormal falling activity monitoring using rank polling. Therefore, the proposed IoT-based smart healthcare video surveillance system using edge computing reduces the network bandwidth and response time and maximizes the fall behavior prediction accuracy significantly comparing to existing cloud-based video surveillance systems.
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References
Abbas Q, Ibrahim MEA, ArfanJaffar M (2018) Video scene analysis: an overview and challenges on deep learning algorithms. Multimed Tools Appl 77:20415–20453. https://doi.org/10.1007/s11042-017-5438-7
Ahmad J, Mehmood I, Baik SW (2017) Efficient object-based surveillance image search using spatial pooling of convolutional features. J Vis Commun Image Represent 45:62–76. https://doi.org/10.1016/j.jvcir.2017.02.010
Alsmirat MA, Jararweh Y, Obaidat I, Gupta BB (2017) Internet of surveillance: a cloud supported large-scale wireless surveillance system. J Supercomput 73:973–992. https://doi.org/10.1007/s11227-016-1857-x
Auvinet E, Rougier C, Meunier J, St-Arnaud A, Rousseau J (2010) Multiple cameras fall dataset. Technical Report 1350, DIRO-Université de Montréal. http://www.iro.umontreal.ca/~labimage/Dataset/
Cermeno E, Perez A, Siguenza JA (2018) Intelligent video surveillance beyond robust background modeling. Expert Syst Appl 91:138–149. https://doi.org/10.1016/j.eswa.2017.08.052
Dedeoglu Y (2004) Moving Object Detection, Tracking and Classification for Smart Video Surveillance. Dissertation, Bilkent University. http://hdl.handle.net/11693/29543
Dhiman C, Vishwakarma DK (2019) A review of state-of-the-art techniques for abnormal human activity recognition. Eng Appl Artif Intell 77:21–45. https://doi.org/10.1016/j.engappai.2018.08.014
Ghorayeb H (2007) Design and implementation of real time computer vision algorithms for video surveillance applications. Mathematics, Ecole Nationale Supérieure des Mines de Paris pastel-00003064:1–217. https://pastel.archives-ouvertes.fr/pastel-00003064
Ghorayeb H (2007) Design and implementation of real time computer vision algorithms for video surveillance applications. Dissertation, HAL Archive-ouvertes. https://pastel.archives-ouvertes.fr/pastel-00003064/document
Grant JM, Flynn PJ (2017) Crowd scene understanding from video: a survey. ACM Trans Multimedia Comput Commun Appl 13:19. https://doi.org/10.1145/3052930
Guo J, Zheng P, Huang J (2017) An efficient motion detection and tracking scheme for encrypted surveillance videos. ACM Trans Multimedia Comput Commun Appl 13:61. https://doi.org/10.1145/3131342
Hu L, Ni Q (2018) IoT-driven automated object detection algorithm for urban surveillance systems in smart cities. IEEE Internet Things J 5:747–754. https://doi.org/10.1109/JIOT.2017.2705560
Kalirajan K, Sudha M (2015) Moving object detection for video surveillance. Sci World J 2015:907469. https://doi.org/10.1155/2015/907469
Kalyanaraman A, Hong D, Soltanaghaei E, Whitehouse K (2017) FormaTrack: tracking People based on Body Shape. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. https://doi.org/10.1145/3130926
Kangin D (2016) Intelligent video surveillance. Dissertation, Lancaster University. http://eprints.lancs.ac.uk/id/eprint/80349
Kim B, Psannis K, Bhaskar H (2017) Special section on emerging multimedia technology for smart surveillance system with IoT environment. J Supercomput 73:923–925. https://doi.org/10.1007/s11227-016-1939-9
Kyrkou C, Christoforou E, Timotheou S, Theocharides T, Panayiotou C, Polycarpou M (2018) Optimizing the detection performance of smart camera networks through a probabilistic image-based model. IEEE T Circ Syst Vid 28:1197–1211. https://doi.org/10.1109/TCSVT.2017.2651362
Lee D, Park N (2017) Geocasting-based synchronization of Almanac on the maritime cloud for distributed smart surveillance. J Supercomput 73:1103–1118. https://doi.org/10.1007/s11227-016-1841-5
Long C, Cao Y, Jiang T, Zhang Q (2018) Edge computing framework for cooperative video processing in multimedia IoT systems. IEEE Trans Multimedia 20:1126–1139. https://doi.org/10.1109/TMM.2017.2764330
Luo X, Liu T, Liu J, Guo X, Wang G (2012) Design and implementation of a distributed fall detection system based on wireless sensor networks. EURASIP J Wirel Commun Netw 2012:118. https://doi.org/10.1186/1687-1499-2012-118
Mano LY et al (2016) Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. Comput Commun 89:178–190. https://doi.org/10.1016/j.comcom.2016.03.010
Orten BB (2005) Moving Object Identification and Event Recognition in Video Surveillance Systems. Dissertation, Middle East Technical University. https://etd.lib.metu.edu.tr/upload/12606294/index.pdf Accessed 11 March 2021
Panda DK (2012) Motion Detection, Object Classification and Tracking for Visual Surveillance Application. Dissertation, National Institute of Technology Rourkela. https://core.ac.uk/download/pdf/53188742.pdf
Park H, Min OG, Lee YJ (2017) Scalable architecture for an automated surveillance system using edge computing. J Supercomput 73:926–939. https://doi.org/10.1007/s11227-016-1750-7
Pirbhulal S, Samuel OW, Wu W, Sangaiah AK, Li G (2019) A joint resource-aware and medical data security framework for wearable healthcare systems. Future Gener Comput Syst 95:382–391. https://doi.org/10.1016/j.future.2019.01.008
Pirbhulal S, Wu W, Muhammad K, Mehmood I, Li G, Albuquerque VHC (2020) Mobility enabled security for optimizing IoT based intelligent applications. IEEE Netw 34:72–77. https://doi.org/10.1109/MNET.001.1800547
Rajavel R, Thangarathanam M (2016) Adaptive probabilistic behavioural learning system for the effective behavioural decision in cloud trading negotiation market. Future Gener Comput Syst 58:29–41. https://doi.org/10.1016/j.future.2015.12.007
Rajavel R, Thangarathanam M (2021) Agent-based automated dynamic SLA negotiation framework in the cloud using the stochastic optimization approach. Appl Soft Comput 101:107040. https://doi.org/10.1016/j.asoc.2020.107040
Rajiv P, Raj R, Chandra M (2016) Email based remote access and surveillance system for smart home infrastructure. Perspect Sci 8:459–461. https://doi.org/10.1016/j.pisc.2016.04.104
Roudposhti KK (2014) Probabilistic-based Human Behaviour Analysis using Hierarchical Framework. Dissertation, University of Coimbra. https://estudogeral.uc.pt/handle/10316/24529
Shao Z, Cai J, Wang Z (2018) Smart monitoring cameras driven intelligent processing to big surveillance video data. IEEE Trans Big Data 4:105–116. https://doi.org/10.1109/TBDATA.2017.2715815
Sun J, Shao J, He C (2019) Abnormal event detection for video surveillance using deep one-class learning. Multimed Tools Appl 78:3633–3647. https://doi.org/10.1007/s11042-017-5244-2
Tsai CW, Liao MY, Yang CS, Chiang MC (2013) Classification algorithms for interactive multimedia services: a review. Multimed Tools Appl 67:137–165. https://doi.org/10.1007/s11042-011-0957-0
Tsakanikas V, Dagiuklas T (2018) Video surveillance systems-current status and future trends. Comput Electr Eng 70:736–753. https://doi.org/10.1016/j.compeleceng.2017.11.011
Xu Z, Mei L, Hu C, Liu Y (2016) The big data analytics and applications of the surveillance system using video structured description technology. Clust Comput 19:1283–1292. https://doi.org/10.1007/s10586-016-0581-x
Zhang H, Zhang H, Pirbhulal S, Wu W, Albuquerque VHC (2020) Active balancing mechanism for imbalanced medical data in deep learning-based classification models. ACM Trans Multimed Comput Commun Appl 16:39. https://doi.org/10.1145/3357253
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Rajavel, R., Ravichandran, S.K., Harimoorthy, K. et al. IoT-based smart healthcare video surveillance system using edge computing. J Ambient Intell Human Comput 13, 3195–3207 (2022). https://doi.org/10.1007/s12652-021-03157-1
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DOI: https://doi.org/10.1007/s12652-021-03157-1