J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2020 , Vol. 25 >Issue 5: 600 - 605

DOI: https://doi.org/10.1007/s12204-020-2202-3

Low-Cost Approach for Improving Video Transmission Efficiency in WVSN

Expand
  • (1. Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System,
    Hubei University of Technology, Wuhan 430068, China; 2. Department of Electrical and Computer Engineering, Rowan
    University, Glassboro, NJ 08028, USA; 3. State Key Laboratory of Management and Control for Complex Systems,
    Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; 4. Institute of Smart Education, Qingdao
    Academy of Intelligent Industries, Qingdao 266041, Shandong, China)

Online published: 2020-09-11

Fold

Abstract

The wireless visual sensor network (WVSN) as a new emerged intelligent visual system, has been
applied in many video monitoring sites. However, there is still great challenge because of the limited wireless
network bandwidth. To resolve the problem, we propose a real-time dynamic texture approach which can detect
and reduce the temporal redundancy during many successive image frames. Firstly, an adaptively learning background
model is improved to discover successive similar image frames from the inputting video sequence. Then,
the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and
background element dynamics. Furthermore, a background discarding strategy based on visual motion coherence
is proposed to determine whether each image frame is streamed or not. To evaluate the trade-off performance
of the proposed method, it is tested on the CDW-2014 dataset, which can accurately detect the first foreground
frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes, and
the misdetection rate of the undetected foreground frames is near to zero. Compared to the original stream, it
can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art
methods.

Key words： wireless visual sensor network (WVSN); temporal redundancy; foreground; background; dynamic
texture

Cite this article

LIU Min, DENG Bin, TANG Ying, WU Minghu, WANG Juan . Low-Cost Approach for Improving Video Transmission Efficiency in WVSN[J]. Journal of Shanghai Jiaotong University(Science), 2020 , 25(5) : 600 -605 . DOI: 10.1007/s12204-020-2202-3

References

[1] STAUFFER C, GRIMSON W E L. Adaptive background mixture models for real-time tracking [C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, CO,USA: IEEE, 1999: 246-252.
[2] OU S H, LEE C H, SOMAYAZULU V S, et al. Online multi-view video summarization for wireless video sensor network [J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(1): 165-179.
[3] CHEN B H, HUANG S C. An advanced moving object detection algorithm for automatic traffic monitoring in real-world limited bandwidth networks [J]. IEEE Transactions on Multimedia, 2014, 16(3): 837-847.
[4] PAEK J, HICKS J, COE S, et al. Image-based environmental monitoring sensor application using an embedded wireless sensor network [J]. Sensors, 2014, 14(9):15981-16002.
[5] CHEN B H, HUANG S C, YEN J Y. Counterpropagation artificial neural network-based motion detection algorithm for static-camera surveillance scenarios[J]. Neurocomputing, 2018, 273: 481-493.
[6] PEIXOTO J P J, COSTA D G. Wireless visual sensor networks for smart city applications: A relevancebased approach for multiple sinks mobility [J]. Future Generation Computer Systems, 2017, 76: 51-62.
[7] HUANG K X, ZHANG Q, ZHOU C J, et al. An efficient intrusion detection approach for visual sensor networks based on traffic pattern learning [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2017, 47(10): 2704-2713.
[8] BRAMBERGER M, DOBLANDER A, MAIER A, et al. Distributed embedded smart cameras for surveillance applications [J]. Computer, 2006, 39(2): 68-75.
[9] ZHANG T, CHOWDHERY A, BAHL P V, et al. The design and implementation of a wireless surveillance system [C]//21st Annual International Conference on Mobile Computing and Networking. Paris, France: Association for Computing Machinery, 2015: 426-438.
[10] MEHMOOD I, SAJJAD M, EJAZ W, et al. Saliencydirected prioritization of visual data in wireless surveillance networks [J]. Information Fusion, 2015, 24: 16-30.
[11] BACHIR A, DOHLERM,WATTEYNE T, et al. MAC essentials for wireless sensor networks [J]. IEEE Communications Surveys & Tutorials, 2010, 12(2): 222-248.
[12] YE Y, CI S, KATSAGGELOS A K, et al. Wireless video surveillance: A survey [J]. IEEE Access, 2013,1: 646-660.
[13] IMRAN M, AHMAD N, KHURSHEED K, et al. Implementation of wireless vision sensor node with a lightweight bi-level video coding [J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems,2013, 3(2): 198-209.
[14] AURANGZEB K, ALHUSSEIN M, HAIDER S I. Impact of complexity and compression ratio of compression method on lifetime of vision sensor node [J]. Elektronika Ir Elektrotechnika, 2017, 23(3): 64-67.
[15] AURANGZEB K, ALHUSSEIN M, O’NILS M. Data reduction using change coding for remote applications of wireless visual sensor networks [J]. IEEE Access,2018, 6: 37738-37747.
[16] SOBRAL A, VACAVANT A. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos [J]. Computer Vision and Image Understanding, 2014, 122: 4-21.
[17] MANIMOZHI S, AASHA N S, RADHA S. Compressed sensing based background subtraction for object detection in WSN [C]//International Conference on Communications and Signal Processing. Melmaruvathur,India: IEEE, 2015: 569-573.
[18] FAYED S, YOUSSEF S M, EL-HELW A, et al. Analytical framework for adaptive compressive sensing for target detection within wireless visual sensor networks[J]. Multimedia Tools and Applications, 2018, 77(13):16533-16559.
[19] NANDHINI S A, RADHA S, KISHORE R. Efficient compressed sensing based object detection system for video surveillance application in WMSN [J]. Multimedia Tools and Applications, 2018, 77(2): 1905-1925.
[20] KHAN M U K, KHAN A, KYUNG C M. EBSCam:Background subtraction for ubiquitous computing [J].IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2017, 25(1): 35-47.
[21] LECCAM, ZOUY,GOTTARDI M, et al. A low power smart camera for video surveillance and forensic applications[C]//International Conference on Engineering,Technology and Innovation. Funchal, Portugal: IEEE,2017: 626-631.
[22] MAKANTASIS K, NIKITAKIS A, DOULAMIS A D, et al. Data-driven background subtraction algorithm for in-camera acceleration in thermal imagery [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(9): 2090-2104.
[23] ELGAMMAL A, HARWOOD D, DAVIS L. Nonparametric model for background subtraction[M]//Computer vision-ECCV 2000. Berlin, Heidelberg:Springer, 2000: 75l-767.
[24] PICCARDI M. Background subtraction techniques:A review [C]//IEEE International Conference on Systems, Man and Cybernetics. Hague, the Netherlands:IEEE, 2004: 3099-3104.
[25] DORETTO G, CHIUSO A, WU Y N, et al. Dynamic textures [J]. International Journal of Computer Vision,2003, 51(2): 91-109.
[26] WU J Y, YUEN C, WANG M, et al. Content-aware concurrent multipath transfer for high-definition video streaming over heterogeneous wireless networks [J].IEEE Transactions on Parallel and Distributed Systems,2016, 27(3): 710-723.
[27] SHANG W, CHENG Y F. An improved OTSU method based on Genetic Algorithm [C]//Proceedings of the 2016 4th International Conference on Machinery,Materials and Information Technology Applications.Paris, France: Atlantis Press, 2016: 1656-1661.

Options
Outlines

/

〈 〉