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.
Similar content being viewed by others
References
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. 353–358
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. 237–245
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. 1–5
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)
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. 56–61
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)
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)
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)
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)
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. 364–367
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)
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)
X. Ji, Z. Wei, Y. Feng, Effective vehicle detection techniques for traffic surveillance systems. J. Vis. Commun. Image Represent. 17(3), 647–658 (2006)
R.E. Kalman, A new approach to linear filtering and prediction problems. Trans. ASME J. Basic Eng. 82, 35–45 (1960)
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)
N.K. Kanhere, Vision-Based Detection, Tracking and Classification of Vehicles Using Stable Features with Automatic Camera Calibration, ed, (2008), p. 105
D.S. Kushwaha, T. Kumar, An efficient approach for detection and speed estimation of moving vehicles. J. Proc. Comput. Sci. 89, 726–731 (2016)
X. Li, Z.Q. Liu, K.M. Leung, Detection of vehicles from traffic scenes using fuzzy integrals. Pattern Recogn. 35(4), 967–980 (2002)
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)
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)
Y. Mary Reeja, T. Latha, W. Rinisha, Detecting and tracking moving vehicles for traffic surveillance. ARPN J. Eng. Appl. Sci. 10(4) (2015)
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)
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)
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)
G. Prathiba, M. Santhi, A. Ahilan, Design and implementation of reliable flash ADC for microwave applications. Microelectron. Reliab. 88–90, 91–97 (2018)
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)
J. Satheesh Kumar, G. Saravana Kumar, A. Ahilan, High performance decoding aware FPGA bit-stream compression using RG codes. Cluster Comput. 1–5 (2018)
J.P. Shinora, K. Muralibabu, L. Agilandeeswari, An adaptive approach for validation in visual object tracking. Proc. Comput. Sci. 58, 478–485 (2015)
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)
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)
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)
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. 1019–1022
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)
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)
L. Wang, F. Chen, H. Yin, Detecting and tracking vehicles in traffic by unmanned aerial vehicles. J. Autom. Constr. 72, 294–308 (2016)
Z. Wei et al., Multilevel framework to detect and handle vehicle occlusion. IEEE Trans. Intell. Transp. Syst. 9, 161–174 (2008)
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)
J. Zhou, D. Gao, D. Zhang, Moving vehicle detection for automatic traffic monitoring. IEEE Trans. Veh. Technol. 56(1), 51–59 (2007)
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)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Appathurai, A., Sundarasekar, R., Raja, C. et al. An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System. Circuits Syst Signal Process 39, 734–756 (2020). https://doi.org/10.1007/s00034-019-01224-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00034-019-01224-9