Advertisement

An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System

  • Ahilan AppathuraiEmail author
  • Revathi Sundarasekar
  • C. Raja
  • E. John Alex
  • C. Anna Palagan
  • A. Nithya
Article
  • 3 Downloads

Abstract

This paper presents an effective traffic video surveillance system for detecting moving vehicles in traffic scenes. Moving vehicle identification process on streets is utilized for vehicle tracking, counts, normal speed of every individual vehicle, movement examination, and vehicle classifying targets and might be executed under various situations. In this paper, we develop a novel hybridization of artificial neural network (ANN) and oppositional gravitational search optimization algorithm (ANN–OGSA)-based moving vehicle detection (MVD) system. The proposed system consists of two main phases such as background generation and vehicle detection. Here, at first, we develop an efficient method to generate the background. After the background generation, we detect the moving vehicle using the ANN–OGSA model. To increase the performance of the ANN classifier, we optimally select the weight value using the OGSA algorithm. To prove the effectiveness of the system, we have compared our proposed algorithm with different algorithms and utilized three types of videos for experimental analysis. The precision of the proposed ANN–OGSA method has been improved over 3% and 6% than the existing GSA-ANN and ANN, respectively. Similarly, the GSA-ANN-based MVD system attained the maximum recall of 89%, 91%, and 91% for video 1, video 2, and video 3, respectively.

Keywords

Moving vehicle detection Artificial neural network Oppositional-based learning Gravitational search optimization algorithm Traffic video surveillance system 

Notes

References

  1. 1.
    A. Ahilan, E.A.K. James, Design and implementation of real time car theft detection in FPGA, in 2011 Third International Conference on Advanced Computing, Chennai (2011), pp. 353–358Google Scholar
  2. 2.
    A. Ahilan, P. Deepa, Improving lifetime of memory devices using evolutionary computing-based error correction coding, in Computational Intelligence, Cyber Security and Computational Models (2016), pp. 237–245Google Scholar
  3. 3.
    A. Ahilan, P. Deepa, Modified Decimal Matrix Codes in FPGA configuration memory for multiple bit upsets, in 2015 International Conference on Computer Communication and Informatics (ICCCI) (2015), pp. 1–5Google Scholar
  4. 4.
    A. Ahilan, P. Deepa, Design for built-in FPGA reliability via fine-grained 2-D error correction codes. Microelectron. Reliab. 55(9–10), 2108–2112 (2015)CrossRefGoogle Scholar
  5. 5.
    A. Appathurai, P. Deepa, Design for reliability: a novel counter matrix code for FPGA based quality applications, in 6 Asia Symposium on Quality Electronic Design (ASQED) (2015), pp. 56–61Google Scholar
  6. 6.
    A. Baher, H. Porwal, W. Recker, Short term freeway traffic flow prediction using genetically optimized time-delay-based neural networks, in Transportation Research Board 78th Annual Meeting, Washington, DC (1999)Google Scholar
  7. 7.
    P.V.K. Borges, N. Conci, A. Cavallaro, Video-based human behavior understanding: a survey. IEEE Trans. Circuits Syst. Video Technol. 23(11), 1993–2008 (2013)CrossRefGoogle Scholar
  8. 8.
    H.-Y. Cheng, C.-C. Weng, Y.-Y. Chen, Vehicle detection in aerial surveillance using dynamic bayesian networks. IEEE Trans. Image Process. 21(4), 2152–2159 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    M. Cheon, W. Lee, C. Yoon, M. Park, Vision-based vehicle detection system with consideration of the detecting location. IEEE Trans. Intell. Transp. Syst. 13(3), 1243–1252 (2012)CrossRefGoogle Scholar
  10. 10.
    H. Chung-Lin, L. Wen-Chieh, A vision-based vehicle identification system, in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, vol. 4 (2004), pp. 364–367Google Scholar
  11. 11.
    W.-C. Hu, C.-Y. Yang, D.-Y. Huang, Robust real-time ship detection and tracking for visual surveillance ofcage aquaculture. J. Vis. Commun. Image Represent. 22(6), 543–556 (2011)CrossRefGoogle Scholar
  12. 12.
    W.-C. Hu, C.-H. Chen, T.-Y. Chen, D.-Y. Huang, Z.-C. Wu, Moving object detection and tracking from video captured by moving camera. J. Vis. Commun. Image Represent. 30, 164–180 (2015)CrossRefGoogle Scholar
  13. 13.
    X. Ji, Z. Wei, Y. Feng, Effective vehicle detection techniques for traffic surveillance systems. J. Vis. Commun. Image Represent. 17(3), 647–658 (2006)CrossRefGoogle Scholar
  14. 14.
    R.E. Kalman, A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960)CrossRefGoogle Scholar
  15. 15.
    N.K. Kanhere, S.T. Birchfield, Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features. IEEE Trans. Intell. Transp. Syst. 9, 148–160 (2008)CrossRefGoogle Scholar
  16. 16.
    N.K. Kanhere, Vision-Based Detection, Tracking and Classification of Vehicles Using Stable Features with Automatic Camera Calibration, ed, (2008), p. 105 Google Scholar
  17. 17.
    D.S. Kushwaha, T. Kumar, An efficient approach for detection and speed estimation of moving vehicles. J. Proc. Comput. Sci. 89, 726–731 (2016)CrossRefGoogle Scholar
  18. 18.
    X. Li, Z.Q. Liu, K.M. Leung, Detection of vehicles from traffic scenes using fuzzy integrals. Pattern Recogn. 35(4), 967–980 (2002)CrossRefzbMATHGoogle Scholar
  19. 19.
    F.-L. Lian, Y.-C. Lin, C.-T. Kuo, J.-H. Jean, Voting-based motion estimation for real-time video transmission in networked mobile camera systems. IEEE Trans. Industr. Inf. 9(1), 172–180 (2013)CrossRefGoogle Scholar
  20. 20.
    A. Lozano, G. Manfredi, L. Nieddu, An algorithm for the recognition of levels of congestion in road traffic problems. Math. Comput. Simul. 79(6), 1926–1934 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Y. Mary Reeja, T. Latha, W. Rinisha, Detecting and tracking moving vehicles for traffic surveillance. ARPN J. Eng. Appl. Sci. 10(4) (2015)Google Scholar
  22. 22.
    N. Messai, P.T. Thomas, D. Lefebvre, A.El. Moudni, Neural networks for local monitoring of traffic magnetic sensors. Control Eng. Pract. 13(1), 67–80 (2005)CrossRefGoogle Scholar
  23. 23.
    S. Movaghati, A. Moghaddamjoo, A. Tavakoli, Road extraction from satellite images using particle filtering and extended Kalman filtering. IEEE Trans. Geosci. Remote Sens. 48(7), 2807–2817 (2010)CrossRefGoogle Scholar
  24. 24.
    X. Niu, A semi-automatic framework for highway extraction and vehicle detection based on a geometric deformable model. ISPRS J. Photogr. Remote Sens. 61(3–4), 170–186 (2006)CrossRefGoogle Scholar
  25. 25.
    G. Prathiba, M. Santhi, A. Ahilan, Design and implementation of reliable flash ADC for microwave applications. Microelectron. Reliab. 88–90, 91–97 (2018)CrossRefGoogle Scholar
  26. 26.
    M. SaiSravana, S. Natarajan, E.S. Krishna, B.J. Kailath, Fast and accurate on-road vehicle detection based on color intensity segregation. J. Proc. Comput. Sci. 133, 594–603 (2018)CrossRefGoogle Scholar
  27. 27.
    J. Satheesh Kumar, G. Saravana Kumar, A. Ahilan, High performance decoding aware FPGA bit-stream compression using RG codes. Cluster Comput. 1–5 (2018) Google Scholar
  28. 28.
    J.P. Shinora, K. Muralibabu, L. Agilandeeswari, An adaptive approach for validation in visual object tracking. Proc. Comput. Sci. 58, 478–485 (2015)CrossRefGoogle Scholar
  29. 29.
    B. Sivasankari, A. Ahilan, R. Jothin, A. Jasmine Gnana Malar, Reliable N sleep shuffled phase damping design for ground bouncing noise mitigation. Microelectron. Reliab. 88–90, 1316–1321 (2018)CrossRefGoogle Scholar
  30. 30.
    G. Somasundaram, R. Sivalingam, V. Morellas, N. Papanikolopoulos, Classification and counting of composite objects in traffic scenes using global and local image analysis. IEEE Trans. Intell. Transp. Syst. 14(1), 69–81 (2013)CrossRefGoogle Scholar
  31. 31.
    D. Srinivasan, M.C. Choy, R.L. Cheu, Neural networks for real time traffic signal control. IEEE Trans. Intell. Transp. Syst. 7(3), 261–272 (2006)CrossRefGoogle Scholar
  32. 32.
    Z. Sun, G. Bebis, R. Miller, On-road vehicle detection using Gabor filters and support vector machines, in Proceedings of the IEEE Conference Digital Signal Processing, vol. 2 (2002), pp. 1019–1022Google Scholar
  33. 33.
    B. Tian, Y. Li, B. Li, D. Wen, Rear-view vehicle detection and tracking by combining multiple parts for complex urban surveillance. IEEE Trans. Intell. Transp. Syst. 15(2) (2014) Google Scholar
  34. 34.
    D. Tran, J. Yuan, D. Forsyth, Video event detection: from subvolume localization to spatiotemporal path search. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 404–416 (2014)CrossRefGoogle Scholar
  35. 35.
    L. Wang, F. Chen, H. Yin, Detecting and tracking vehicles in traffic by unmanned aerial vehicles. J. Autom. Constr. 72, 294–308 (2016)CrossRefGoogle Scholar
  36. 36.
    Z. Wei et al., Multilevel framework to detect and handle vehicle occlusion. IEEE Trans. Intell. Transp. Syst. 9, 161–174 (2008)CrossRefGoogle Scholar
  37. 37.
    W. Zhang, X.Z. Fang, X. Yang, Moving vehicles segmentation based on Bayesian framework for Gaussian motion model. Pattern Recogn. Lett. 27(1), 956–967 (2006)CrossRefGoogle Scholar
  38. 38.
    J. Zhou, D. Gao, D. Zhang, Moving vehicle detection for automatic traffic monitoring. IEEE Trans. Veh. Technol. 56(1), 51–59 (2007)CrossRefGoogle Scholar
  39. 39.
    X. Zhou, C. Yang, W. Yu, Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 597–610 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Infant Jesus College of EngineeringTuticorinIndia
  2. 2.Anna UniversityChennaiIndia
  3. 3.Koneru Lakshmaiah Education FoundationVaddeswaramIndia
  4. 4.CMR Institute of TechnologyHyderabadIndia
  5. 5.Malla Reddy Engineering CollegeHyderabadIndia
  6. 6.Vaagdevi College of EngineeringWarangalIndia

Personalised recommendations