Skip to main content
SpringerLink
Log in

Intelligent monitor system based on cloud and convolutional neural networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Nowadays, cloud-based services are widely developed. The deployment of cloud technology has boosted the development and application of web services. It reduces the overhead of software virtual machine, and supports a wider range of operating systems. Moreover, it enhances the utilization of infrastructure. With the development of artificial intelligence (AI) technology, especially artificial neural network (ANN), intelligent monitor systems are being raised and developed in our daily life. However, a simple task with a single ANN costs a lot of time and computation resources. Hence, we propose using a cloud-based system to share computation resources for ANN to reduce redundant computation. In this paper, we present an intelligent monitor system, which is based on cloud technology, to provide intelligent monitor services. The system is designed with hybrid convolutional neural networks. It has been used for several intelligent monitor tasks, such as scene change detection, stranger recognition, facial expression recognition and action recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://opencv.org/.

  2. http://www.nada.kth.se/cvap/actions/.

References

  1. El-Shazly EH, Abdelwahab MM, Taniguchi RI (2015) Efficient facial and facial expression recognition using canonical correlation analysis for transform domain features fusion and classification. In: 2015 11th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp 639–644. doi:10.1109/SITIS.2015.57

  2. Deshmukh S, Patwardhan M, Mahajan A (2016) Survey on real-time facial expression recognition techniques. IET Biometr 5(3):155. doi:10.1049/iet-bmt.2014.0104

    Google Scholar 

  3. Chen J, Chen Z, Chi Z, Fu H (2016) Facial expression recognition in video with multiple feature fusion. IEEE Trans Affect Comput PP(99):1. doi:10.1109/TAFFC.2016.2593719

    Google Scholar 

  4. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527. doi:10.1162/neco.2006.18.7.1527

    Article  MathSciNet  MATH  Google Scholar 

  5. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc., pp 1097–1105. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf. Accessed 6 Dec 2012

  6. Bappy JH, Roy-Chowdhury AK (2016) CNN based region proposals for efficient object detection. In: 2016 IEEE International Conference on Image Processing (ICIP), pp 3658–3662. doi:10.1109/ICIP.2016.7533042

  7. Basaeed E, Bhaskar H, Al-Mualla M (2015) CNN-based multi-band fused boundary detection for remotely sensed images. In: 6th International Conference on Imaging for Crime Prevention and Detection (ICDP-15), pp 1–6. doi:10.1049/ic.2015.0109

  8. Chen L, Wang S, Fan W, Sun J, Naoi S (2015) Beyond human recognition: a CNN-based framework for handwritten character recognition. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp 695–699. doi:10.1109/ACPR.2015.7486592

  9. Gidaris S, Komodakis N (2015) Object detection via a multi-region and semantic segmentation-aware CNN model. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp 1134–1142. doi:10.1109/ICCV.2015.135

  10. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278. doi:10.1109/5.726791

    Article  Google Scholar 

  11. Chalaemwongwan N, Kurutach W (2016) Mobile cloud computing: a survey and propose solution framework. In: 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp 1–4. doi:10.1109/ECTICon.2016.7561437

  12. Celesti A, Fazio M, Celesti F, Sannino G, Campo S, Villari M (2016) New trends in biotechnology: the point on NGS Cloud computing solutions. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp 267–270. doi:10.1109/ISCC.2016.7543751

  13. Guang-ping Y, Jing-yang W, Ming-zhe Y, Yang Y (2015) Research on prediction method of sludge bulking based on ANN and grey Markov model. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp 1622–1627. doi:10.1109/CYBER.2015.7288188

  14. Belhouchet K, Bayadi A, Bendib ME (2015) Artificial neural networks (ANN) and genetic algorithm modeling and identification of arc parameter in insulators flashover voltage and leakage current. In: 2015 4th International Conference on Electrical Engineering (ICEE), pp 1–6. doi:10.1109/INTEE.2015.7416698

  15. Buch H, Dudani KK, Pipalava DP (2015) Prediction of total transfer capability using ANN in restructured power system. In: 2015 5th Nirma University International Conference on Engineering (NUiCONE), pp 1–5. doi:10.1109/NUICONE.2015.7449620

  16. Barman D, Chowdhury N, Singha RK (2012) To predict possible profit/loss of a movie to be launched using MLP with back-propagation learning. In: 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS), pp 322–325. doi:10.1109/CODIS.2012.6422203

  17. Azarloo A, Farokhi F (2012) Automatic musical instrument recognition using K-NN and MLP neural networks. In: 2012 Fourth International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), pp 289–294. doi:10.1109/CICSyN.2012.61

  18. Alves EDS, Filho JBOS, Galliez RM, Kritski A (2013) Specialized MLP classifiers to support the isolation of patients suspected of pulmonary tuberculosis. In: 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, pp 40–45. doi:10.1109/BRICS-CCI-CBIC.2013.18

  19. Kumarawadu S, Watanabe K, Kiguchi K, Izumi K (2002) Adaptive output tracking of partly known robotic systems using SoftMax function networks. In: Proceedings of the 2002 International Joint Conference on Neural Networks, 2002. IJCNN ’02, vol 1, pp 483–488. doi:10.1109/IJCNN.2002.1005520

  20. Ahmed N, Campbell M (2011) Variational Bayesian learning of probabilistic discriminative models with latent softmax variables. IEEE Trans Signal Process 59(7):3143. doi:10.1109/TSP.2011.2144587

    Article  MathSciNet  Google Scholar 

  21. Cao Z, Principe JC, Ouyang B, Dalgleish F, Vuorenkoski A (2015) Marine animal classification using combined CNN and hand-designed image features. In: OCEANS 2015—MTS/IEEE Washington, pp 1–6

  22. Albora AM (2016) Wave-CNN method approach of archaeogeophysics studies. In: 2016 24th Signal Processing and Communication Application Conference (SIU), pp 173–176. doi:10.1109/SIU.2016.7495705

  23. Masci J, Meier U, Ciresan D, Schmidhuber J, Fricout G (2012) Steel defect classification with max-pooling convolutional neural networks. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp 1–6. doi:10.1109/IJCNN.2012.6252468

  24. Masci J, Giusti A, Ciresan D, Fricout G, Schmidhuber J (2013) A fast learning algorithm for image segmentation with max-pooling convolutional networks. In: 2013 IEEE International Conference on Image Processing, pp 2713–2717. doi:10.1109/ICIP.2013.6738559

  25. Dahl GE, Sainath TN, Hinton GE (2013) Improving deep neural networks for LVCSR using rectified linear units and dropout. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 8609–8613. doi:10.1109/ICASSP.2013.6639346

  26. Hara K, Saito D, Shouno H (2015) Analysis of function of rectified linear unit used in deep learning. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp 1–8. doi:10.1109/IJCNN.2015.7280578

  27. Xiong S, Guo W, Liu D (2014) The Vietnamese speech recognition based on rectified linear units deep neural network and spoken term detection system combination. In: 2014 9th International Symposium on Chinese Spoken Language Processing (ISCSLP), pp 183–186. doi:10.1109/ISCSLP.2014.6936574

  28. Alberto AC, Limeira GA, Nadal J (2015) A principal component analysis approach to heart rate turbulence assessment in Chagas disease. In: 2015 Computing in Cardiology Conference (CinC), pp 681–684. doi:10.1109/CIC.2015.7411002

  29. Meraoumia A, Korichi M, Chitroub S, Bouridane A (2015) Hidden Markov models principal component analysis for multispectral palmprint identification. In: 2015 5th International Conference on Information Communication Technology and Accessibility (ICTA), pp 1–6. doi:10.1109/ICTA.2015.7426898

  30. Guo-rong Z, Chang-zhen X, Yan Z (2011) A method of embedded video surveillance based on OpenCV. In: 2011 International Conference on E-Business and E-Government (ICEE), pp 1–4. doi:10.1109/ICEBEG.2011.5881671

  31. Chaczko Z, Braun R (2010) Teaching Computer Vision for telemedicine systems using OpenCV. In: 2010 9th International Conference on Information Technology Based Higher Education and Training (ITHET), pp 17–20. doi:10.1109/ITHET.2010.5480069

  32. Amri SMA, Guan L (2016) Infrastructure as a service: exploring network access control challenges. In: 2016 SAI Computing Conference (SAI), pp 596–603. doi:10.1109/SAI.2016.7556042

  33. Dawoud W, Takouna I, Meinel C (2010) Infrastructure as a service security: challenges and solutions. In: 2010 The 7th International Conference on Informatics and Systems (INFOS), pp 1–8

  34. Akinbi A, Pereira E, Beaumont C (2013) Evaluating security mechanisms implemented on public platform-as-a-service cloud environments case study: Windows Azure. In: 2013 8th International Conference for Internet Technology and Secured Transactions (ICITST), pp 162–167. doi:10.1109/ICITST.2013.6750183

  35. Boniface M, Nasser B, Papay J, Phillips SC, Servin A, Yang X, Zlatev Z, Gogouvitis SV, Katsaros G, Konstanteli K, Kousiouris G, Menychtas A, Kyriazis D (2010) Platform-as-a-service architecture for real-time quality of service management in clouds. In: 2010 Fifth International Conference on Internet and Web Applications and Services (ICIW), pp 155–160. doi:10.1109/ICIW.2010.91

  36. Tian W, Su S, Lu G (2010) A framework for implementing and managing platform as a service in a virtual cloud computing lab. In: 2010 Second International Workshop on Education Technology and Computer Science (ETCS), vol 2, pp 273–276. doi:10.1109/ETCS.2010.126

  37. Afify YM, Moawad IF, Badr NL, Tolba MF (2013) A semantic-based Software-as-a-Service (SaaS) discovery and selection system. In: 2013 8th International Conference on Computer Engineering Systems (ICCES), pp 57–63. doi:10.1109/ICCES.2013.6707171

  38. Akande AO, Belle JPV (2014) Cloud computing in higher education: a snapshot of software as a service. In: 2014 IEEE 6th International Conference on Adaptive Science Technology (ICAST), pp 1–5. doi:10.1109/ICASTECH.2014.7068111

  39. Alkalbani AM, Ghamry AM, Hussain FK, Hussain OK (2016) Sentiment analysis and classification for software as a service reviews. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), pp 53–58. doi:10.1109/AINA.2016.148

  40. Çam BC, Yalçın M, Yavuz HS (2016) Facial expression recognition based on locational features. In: 2016 24th Signal Processing and Communication Application Conference (SIU), pp 1449–1452. doi:10.1109/SIU.2016.7496023

  41. Agada R, Yan J (2015) A model of local binary pattern feature descriptor for valence facial expression classification. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp 634–639. doi:10.1109/ICMLA.2015.185

  42. Apte A, Basavaraj A, N.R. K, (2015) Efficient facial expression ecognition and classification system based on morphological processing of frontal face images. In: 2015 IEEE 10th International Conference on Industrial and Information Systems (ICIIS), pp 366–371. doi:10.1109/ICIINFS.2015.7399039

  43. Borah S, Konwar S (2014) ANN based human facial expression recognition in color images. In: 2014 International Conference on High Performance Computing and Applications (ICHPCA), pp 1–6. doi:10.1109/ICHPCA.2014.7045337

  44. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929. http://dl.acm.org/citation.cfm?id=2627435.2670313

  45. Ji S, Xu W, Yang M, Yu K (2013) 3D convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221. doi:10.1109/TPAMI.2012.59

    Article  Google Scholar 

  46. Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107. doi:10.1145/1327452.1327492

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Dongguan’s Recruitment of Innovation and entrepreneurship talent program, National Natural Science Foundation of China under Grant Nos. 61402210 and 60973137, Program for New Century Excellent Talents in University under Grant No. NCET-12-0250, Strategic Priority Research Program of the Chinese Academy of Sciences with Grant No. XDA03030100, Gansu Sci. and Tech. Program under Grant Nos. 1104GKCA049, 1204GKCA061 and 1304GKCA018, Google Research Awards and Google Faculty Award, China.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China

    Binbin Yong, Gaofeng Zhang & Qingguo Zhou

  2. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Huaming Chen

Authors
  1. Binbin Yong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gaofeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Huaming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qingguo Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingguo Zhou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yong, B., Zhang, G., Chen, H. et al. Intelligent monitor system based on cloud and convolutional neural networks. J Supercomput 73, 3260–3276 (2017). https://doi.org/10.1007/s11227-016-1934-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-016-1934-1

Keywords

Search

Navigation