Hybrid Method for Vehicle Detection from CCTV Captured Image

Abstract:

This paper presents a hybrid method for vehicle detection from CCTV captured image. In order to overwhelm such complex details of the color image, the system combines artificial intelligence techniques to achieve automatic vehicle detection. These are techniques 2D principal component analysis (2DPCA), Fuzzy adaptive resonance theory (Fuzzy ART), genetic algorithm (GA) and self-organizing map. The proposed system can detect different vehicle sizes from different proportional image area. Bilinear interpolation is used to resize each proportional image area to vehicle feature matrix. The proposed system can detect various types of vehicles from the difference image background.

• Periodical: Advanced Materials Research (Volume 677)
• Pages: 412-417
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.677.412
• Online since: March 2013
• Authors: Suppatoomsin Chompoo, Srikaew Arthit
• Keywords: 2DPCA, Fuzzy Art, Genetic Algorithm (GA), Self-Organizing Maps, Vehicle Detection

References

[1] L. Tsai, J. Hsieh and K. Fan: Vehicle Detection Using Normalized Color and Edge Map (IEEE Trans on image processing 2007).

DOI: 10.1109/tip.2007.891147

[2] J. WU, A. YANG, J. WU, A. LIU: Virtual line group based video vehicle detection algorithm utilizing both luminance and chrominance (IEEE databases 2007).

DOI: 10.1109/iciea.2007.4318934

[3] Y. Iwasaki, Y. Kurogi: Real-time robust vehicle detection through the same algorithm both day and night (Proc. Wavelet Analysis and Pattern Recognition 2007).

DOI: 10.1109/icwapr.2007.4421579

[4] Z. Sun, G. Bebis, and R. Miller: On-Road Vehicle Detection Using Evolutionary Gabor Filter Optimization (IEEE trans on intelligent transportation systems 2005).

DOI: 10.1109/tits.2005.848363

[5] H. Cheng, N. Zheng, C. Sun: Boosted Gabor Features Applied to Vehicle Detection (Proc. Pattern Recognition 2006).

DOI: 10.1109/icpr.2006.335

[6] Z. Sun, G. Bebis, and R. Miller: Monocular Precrash Vehicle Detection: Features and Classifiers (IEEE trans on image processing 2006).

DOI: 10.1109/tip.2006.877062

[7] Y. Du, F. Yuan: Real-time Vehicle Tracking by Kalman filtering and Gabor Decomposition (Trans on Information Science and Engineering 2009).

DOI: 10.1109/icise.2009.869

[8] R. Taktak, M. Dufaut, and R. Husson: Road modeling and vehicle detection by using image processing (IEEE databases 1994).

DOI: 10.1109/icsmc.1994.400183

[9] X. Pan, Y. Guo and A. Men: Traffic Surveillance System for Vehicle Flow Detection (Trans on Computer Modeling and Simulation 2010).

DOI: 10.1109/iccms.2010.75

[10] A. Giachetti, M. Campani and V. Torre: The use of optical flow for road navigation (IEEE trans Robot Autom 1998).

DOI: 10.1109/70.660838

[11] W. Kruger, W. Enkelmann and S. Rossle: Real-time estimation and tracking of optical flow vectors for obstacle detection (Proc. IEEE Intelligent Vehicle Symp. 1995).

DOI: 10.1109/ivs.1995.528298

[12] J. Yang, Y. Wang, G. Ye, A. Sowmya, B. Zhang and J. Xu: Feature clustering for vehicle detection and tracking in road traffic surveillance (IEEE databases 2009).

DOI: 10.1109/icip.2009.5413526

[13] M. Pedro, Ferreira, G. Marques, P. Jorge, A. Abrantes and A. Amador: Automatic Vehicle Detection and Classification (Proc. IEEE Intelligent Transportation Systems 2008).

DOI: 10.1109/itsc.2008.4732682

[14] S. Chen, J. Hsieh, J. Wu, and Y. Chen: Vehicle Retrieval Using Eigen Color and Multiple Instance Learning (Trans on Intelligent Information Hiding and Signal Processing 2009).

DOI: 10.1109/iih-msp.2009.304

[15] R. Bellman: Introduction to matrix analysis, 2nd Edition, McGraw-Hill, USA. (1970), p.96.

[16] J. Yang, D. Zhang, A. Frangi, and J. Yang: Two-dimensional PCA: A new approach to appearance-based face representation and recognition (IEEE Trans on pattern analysis and machine intelligence 2004).

DOI: 10.1109/tpami.2004.1261097

[17] A. Srikaew: Genetic Algorithms-Part I, Suranaree Journal of Science and Technology, Vol. 9 (2002), pp.69-83.

[18] G. Peng: Image processing: interpolation (2004).

[19] G.A. Carpenter, S. Grossberg and D.B. Rosen: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Networks (1991), pp.759-771.

DOI: 10.1016/0893-6080(91)90056-b

[20] F. Alilat, S. Loumi and B. Sansal: A new learning algorithm for the fuzzy adaptive resonance theory: Multispectral classification of the Algiers's Bay (LADIS trans on computer science and information system Vol. 4).