Skip to main content
Log in

Progressive Streaming of Video Data for Traffic Surveillance

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Traffic surveillance has been one of the essential attributes in smart city concept. Nowadays, in such applications rotating camera is preferred over static camera. Motivation behind this substitution is to reduce the cost of data transmission and Total of cost of ownership. To design an optimal and performant wireless ‘smart city area network’ for video surveillance systems, this paper focuses on some key areas, namely, transmission efficiency, lossless video data coding, data congestion, edge computing at transmission nodes. The end objective is to achieve high quality received video stream in spite of compressed data transmission. Some research initiatives in this domain are pertinent. For example, Structural Similarity Index (SSIM) based rate distortion optimization is an effective tool in enhancing the perceptual video quality in wireless environments. However, prevailing system does not consider the network congestion conditions, affecting quality of received video. Also, effect of distortion introduced by ‘channel noise’ is unattended. This motivated us to propose a new dual metric traffic control mechanism, wherein both metrics i.e. distortion and data congestion are considered. It is based on an ‘improvised SSIM’ method which incorporates the ‘Rate of allocation’ algorithm as a function. Experimental results unveil that the proposed traffic control using similarity index under noise diversity can achieve better video quality and more data throughput.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31

Similar content being viewed by others

References

  1. Patil, S., Sanyal, R., & Prasad, R. (2015). Efficient video coding in region prediction in online video surveillance. In The 2015 International Conference on Image Processing, Computer Vision, & Pattern Recognition (IPCV), pp. 210–216.

  2. Zhao, P., Liu, Y., Liu, J., Ci, S., & Yao, R. (2014). SSIM-based error-resilient rate-distortion optimization of H.264/AVC video coding for wireless streaming. Signal Processing: Image Communication, 29, 303–315.

    Article  Google Scholar 

  3. Zhao, M. C., Gong, X. Y., Que, X. R., Wang, W. D., & Cheng, S. D. (2012). Context-aware adaptive active queue management mechanism for improving video transmission over IEEE 802.11E WLAN. The Journal of China Universities of Posts and Telecommunications, 19(Suppl. 2), 65–72.

    Article  Google Scholar 

  4. Sankisa, A., Pandremmenou, K., Pahalawatta, P. V., Kondi, L. P., & Katsaggelos, A. K. (2016). SSIM-based distortion estimation for optimized video transmission over inherently noisy channels. International Journal of Multimedia Data Engineering and Management, 7(3), 34–52.

    Article  Google Scholar 

  5. Ukhanova, A., Belyaev, E., Wang, L., & Forchhammer, S. (2012). Power consumption analysis of constant bit rate video transmission over 3G networks. Computer Communications, 35, 1695–1706.

    Article  Google Scholar 

  6. Rezende, C., Mammeri, A., Boukerche, A., & Loureiro, A. A. (2014). A receiver-based video dissemination solution for vehicular networks with content transmissions decoupled from relay node selection. Ad Hoc Networks, 17, 1–17.

    Article  Google Scholar 

  7. Nejad, A. E., & Romouzi, M. (2014). Investigation of video streaming application: a practical guidance. Centre for Info Bio Technology, Indian Journal of Fundamental and Applied Life Sciences, 4(S4), 399–407.

    Google Scholar 

  8. Qadri, N. N., Fleury, M., Altaf, M., & Ghanbari, M. (2010). Multi-source video streaming in a wireless vehicular ad hoc network. The Institution of Engineering and Technology, 4, 1300–1311.

    Google Scholar 

  9. Kazemian, H. B., & Ouazzane, K. (2013). Neuro-Fuzzy approach to video transmission over ZigBee. Neurocomputing, 104, 127–137.

    Article  Google Scholar 

  10. Koutsia, A., Semertzidis, T., Dimitropoulos, K., Grammalidis, N., & Georgouleas, K. (2008). Intelligent Traffic Monitoring and Surveillance with Multiple Cameras. IEEE.

  11. Hassan, M. M., & Hoong, P. K. (2013). Seamless handover integrated solution for video transmission over proxy mobile IPv6 in a micro mobility domain. Journal of Network and Computer Applications, 36, 66–76.

    Article  Google Scholar 

  12. Lee, S., & Chung, K. (2008). Combining the rate adaptation and quality adaptation schemes for wireless video streaming. Journal of Visual Communication and Image Representation, 19, 508–519.

    Article  Google Scholar 

  13. Parameswaran, V., Kannur, A., & Li, B. (2009). Adapting quantization offset in multiple description coding for error resilient video transmission. Journal of Visual Communication and Image Representation, 20, 491–503.

    Article  Google Scholar 

  14. Lei, Z., & Georganas, N. D. (2005). Adaptive video transcoding and streaming over wireless channels. The Journal of Systems and Software, 75, 253–270.

    Article  Google Scholar 

  15. Xing, M., & Cai, L. (2012). Adaptive video streaming with inter-vehicle relay for highway VANET scenario. In IEEE ICC—Wireless Networks Symposium.

  16. Moorthy, A. K., & Bovik, A. C. (2009). A motion compensated approach to video quality assessment. In IEEE Asilomar Conference on Signals, Systems and Computers, pp. 872–875.

  17. Seshadrinathan, K., & Bovik, A. C. (2007). A structural similarity metric for video based on motion Models. In IEEE International Conference on Acoustics, Speech and Signal Processing (Vol. 1, pp. 869–72), Honolulu, HI, USA.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shivprasad P. Patil.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patil, S.P., Sanyal, R. & Prasad, R. Progressive Streaming of Video Data for Traffic Surveillance. Wireless Pers Commun 100, 283–309 (2018). https://doi.org/10.1007/s11277-017-5066-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-5066-6

Keywords

Navigation