SpringerLink
Log in

Multimedia Sensor Network Supported IoT Service

  • Chapter
  • First Online:
Multimedia Sensor Networks

Part of the book series: Advances in Computer Science and Technology ((ACST))

  • 395 Accesses

Abstract

Because of the ability to ubiquitously capture multimedia content from the environment, multimedia sensor networks have great potential for strengthening the traditional wireless sensor networks applications, as well as creating a series of new applications. After introducing three typical service patterns of IoT, information publish service, sensing-controlling service, and IoT search service, this chapter focuses on the IoT search and proposes a progressive search paradigm, which contains three important search strategies: (1) coarse-to-fine search in feature space; (2) near-to-distant search in spatial-temporal space; and (3) low-to-high permission search in the security space. This chapter also proposes a progressive vehicle re-identification framework based on deep neural networks.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In order to save the economic cost, the audio capturing module is integrated in the main processing module.

  2. 2.

    The latest version of the VeRi dataset can be obtained from https://github.com/VehicleReId/VeRidataset.

References

  • Bromley, J., Bentz, J.W., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Säckinger, E., Shah, R.: Signature verification using a siamese time delay neural network. Int. J. Pattern Recognit. Artif. Intell. 7(04), 669–688 (1993)

    Article  Google Scholar 

  • Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: Delving deep into convolutional nets. ar**v preprint ar**v:1405.3531 (2014)

    Google Scholar 

  • Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  • Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (alpr): A state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 23(2), 311–325 (2013)

    Article  Google Scholar 

  • Feris, R.S., Siddiquie, B., Petterson, J., Zhai, Y., Datta, A., Brown, L.M., Pankanti, S.: Large-scale vehicle detection, indexing, and search in urban surveillance videos. IEEE Trans. Multimedia 14(1), 28–42 (2012)

    Article  Google Scholar 

  • Foley, D.H., Sammon, J.W.: An optimal set of discriminant vectors. IEEE Trans. Comput. 100(3), 281–289 (1975)

    Article  Google Scholar 

  • Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Mikolov, T., et al.: Devise: A deep visual-semantic embedding model. In: Proceedings of in Advances in Neural Information Processing Systems (2013)

    Google Scholar 

  • Girshick, R.: Fast r-cnn. In: Proceedings of IEEE International Conference on Computer Vision (2015)

    Google Scholar 

  • Guo, Y.F., Wu, L., Lu, H., Feng, Z., Xue, X.: Null foley-sammon transform. Pattern Recogn. 39(11), 2248–2251 (2006)

    Article  Google Scholar 

  • Hu, W., **e, N., Li, L., Zeng, X., Maybank, S.: A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. (Applications and Reviews) 41(6), 797–819 (2011)

    Google Scholar 

  • Javed, O., Shafique, K., Rasheed, Z., Shah, M.: Modeling inter-camera space-time and appearance relationships for tracking across non-overlap** views. Comput. Vis. Image Underst. 109(2), 146–162 (2008)

    Article  Google Scholar 

  • Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: Proceedings of ACM International Conference on Multimedia (2014)

    Google Scholar 

  • Kettnaker, V., Zabih, R.: Bayesian multi-camera surveillance. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (1999)

    Google Scholar 

  • Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems (2012)

    Google Scholar 

  • Liao, S., Hu, Y., Zhu, X., Li, S.Z.: Person re-identification by local maximal occurrence representation and metric learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Proceedings (2015)

    Google Scholar 

  • N. Li, Jain, J.J., Busso, C.: Modeling of driver behavior in real world scenarios using multiple noninvasive sensors. IEEE Trans. Multimedia 15(5), 1213–1225 (2013)

    Google Scholar 

  • Liu, W., Mei, T., Zhang, Y., Che, C., Luo, J.: Multi-task deep visual-semantic embedding for video thumbnail selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  • Liu, X., Liu, W., Mei, T., Ma, H.: A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: Proceedings of European Conference on Computer Vision (2016a)

    Google Scholar 

  • Liu, X., Liu, W., Mei, T., Ma, H.: A deep learning-based approach to progressive vehicle re-identification for urban surveillance. In: Proceedings of European Conference on Computer Vision (2016b)

    Google Scholar 

  • Liu, X., Liu, W., Ma, H., Fu, H.: Large-scale vehicle re-identification in urban surveillance videos. In: Proceedings of IEEE International Conference on Multimedia and Expo (2016c)

    Google Scholar 

  • Liu, H., Tian, Y., Yang, Y., Pang, L., Huang, T.: Deep relative distance learning: Tell the difference between similar vehicles. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2016d)

    Google Scholar 

  • Liu, X., Liu, W., Mei, T., Ma, H.: PROVID: Progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans. Multimedia 20(3), 645–658 (2018)

    Article  Google Scholar 

  • Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  • Ma, H.: Internet of things: Objectives and scientific challenges. J. Comput. Sci. Technol. 26(6), 919–924 (2011)

    Article  Google Scholar 

  • Ma, H., Liu, W.: Progressive search paradigm for internet of things. In: Proceedings of IEEE MultiMedia (2017)

    Google Scholar 

  • Ma, H., Liu, L., Zhou, A., Zhao, D.: On networking of internet of things: Explorations and challenges. IEEE Internet Things J. 3(4), 441–452 (2016)

    Article  Google Scholar 

  • Matei, B.C., Sawhney, H.S., Samarasekera, S.: Vehicle tracking across nonoverlap** cameras using joint kinematic and appearance features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2011)

    Google Scholar 

  • Mei, T., Rui, Y., Li, S., Tian, Q.: Multimedia search reranking: A literature survey. ACM Comput. Surv. (CSUR) 46(3), 1–38 (2014)

    Google Scholar 

  • Meng, J., Yuan, J., Yang, J., Wang, G., Tan, Y.P.: Object instance search in videos via spatio-temporal trajectory discovery. IEEE Trans. Multimedia 18(1), 116–127 (2016)

    Article  Google Scholar 

  • Romer, K., Ostermaier, B., Mattern, F., Fahrmair, M., Kellerer, W.: (2010), Real-time search for real-world entities: A survey. In: Proceedings of the IEEE

    Google Scholar 

  • Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  • Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proceedings of IEEE International Conference on Computer Vision (2003)

    Google Scholar 

  • Song, J., Yang, Y., Huang, Z., Shen, H.T., Luo, J.: Effective multiple feature hashing for large-scale near-duplicate video retrieval. IEEE Trans. Multimedia 15(8), 1997–2008 (2013)

    Article  Google Scholar 

  • Sunderrajan, S., Manjunath, B.: Context-aware hypergraph modeling for re-identification and summarization. IEEE Trans. Multimedia 18(1), 51–63 (2016)

    Article  Google Scholar 

  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  • Valera, M., Velastin, S.A.: Intelligent distributed surveillance systems: A review. IEEE Proc. Vis. Image Signal Proces. 152(2), 192–204 (2005)

    Article  Google Scholar 

  • Van De Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009)

    Article  MathSciNet  Google Scholar 

  • Wen, Y., Lu, Y., Yan, J., Zhou, Z., Von Deneen, K.M., Shi, P.: An algorithm for license plate recognition applied to intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 12(3), 830–845 (2011)

    Article  Google Scholar 

  • **e, L., Wang, J., Zhang, B., Tian, Q.: Fine-grained image search. IEEE Trans. Multimedia 17(5), 636–647 (2015)

    Article  Google Scholar 

  • Xu, J., Jagadeesh, V., Ni, Z., Sunderrajan, S., Manjunath, B.: Graph-based topic-focused retrieval in distributed camera network. IEEE Trans. Multimedia 15(8), 2046–2057 (2013)

    Article  Google Scholar 

  • Yang, L., Luo, P., Loy, C.C., Tang, X.: A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  • Zapletal, D., Herout, A.: Vehicle re-identification for automatic video traffic surveillance. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (2016)

    Google Scholar 

  • Zhang, D., Yang, L.T., Huang, H.: Searching in internet of things: Vision and challenges. In: Proceedings of IEEE International Symposium on Parallel and Distributed Processing with Applications (2011)

    Google Scholar 

  • Zhang, J., Wang, F.Y., Wang, K., Lin, W.H., Xu, X., Chen, C.: Data-driven intelligent transportation systems: A survey. IEEE Trans. Intell. Transp. Syst. 12(4), 1624–1639 (2011)

    Article  Google Scholar 

  • Zhang, C., Liu, W., Ma, H., Fu, H.: Siamese neural network based gait recognition for human identification. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (2016)

    Google Scholar 

  • Zhang, L., **ang, T., Gong, S.: Learning a discriminative null space for person reidentification. In: Proceedings of IEEE International Conference on Computer Vision (2016)

    Google Scholar 

  • Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: Concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5(3), 222–235 (2014)

    Google Scholar 

  • Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5(38), 1–55 (2014)

    Google Scholar 

  • Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: A benchmark. In: Proceedings of IEEE International Conference on Computer Vision (2015)

    Google Scholar 

  • Zhou, Y., De, S., Wang, W., Moessner, K.: Search techniques for the web of things: A taxonomy and survey. Sensors 16(5), 600 (2016)

    Article  Google Scholar 

  • Ziegeldorf, J.H., Morchon, O.G., Wehrle, K.: Privacy in the internet of things: threats and challenges. Security Commun. Netw. 7(12), 2728–2742 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Bei**g University of Posts and Telecommunications, Bei**g, China

    Huadong Ma, Liang Liu & Hong Luo

Authors
  1. Huadong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ma, H., Liu, L., Luo, H. (2021). Multimedia Sensor Network Supported IoT Service. In: Multimedia Sensor Networks. Advances in Computer Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-16-0107-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-0107-1_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0106-4

  • Online ISBN: 978-981-16-0107-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation