Vehicle Detection in Different Environments

Document Type : Original Article

Authors

1 Department of engineering,. Faculty of Electrical and Computer Engineering. Kharazmi University. Tehran,

2 Malek Ashtar University of Technology, Tehran, Iran

3 Phd student of Artificial intelligence , Islamic Azad University, Lahijan, Iran

Abstract

In this paper, we presented a vehicle detection method based on RGB color space components analysis. The proposed approach is mainly focused on designing the system which is applicable in the case of different weather conditions (rainy, snowy, misty etc), different times during the day and night (daylight, night, noon, afternoon), heavy traffics, the existence of the shadows and different road conditions. Most of the vehicle detection methods utilized background model generation. Since even slight changing in the brightness could decrease the detection quality, in these kinds of methods the background image needs to continuously be updated. In this paper, we presented the method in which the vehicle detection process is performed without any need to generate and update the background model. In the presented approach, we utilized the histogram normalization in order to alleviate the problems caused by brightness change in the case of different weather conditions. We also extracted moving objects using optical flow. Finally, we utilized the HOG descriptor and SVM classifier in order to detect vehicle objects. The performance of the proposed method is tested using VDTD dataset and the results illustrate that the proposed method provides acceptable results specially in heavy traffics and different weather conditions.

Keywords

References

مراجع

[1] Tsai L., Hsieh J., and Fan k., "Vehicle Detection Using Normalized Color and Edge Map", IEEE Transactions on Image Processing, Vol. 16, No. 3, March 2007.
[2] Zhang W., Wu Q. M. J., and Yang X., "Multilevel framework to detect and handle vehicle occlusion", IEEE Trans. Intell. Transp. Syst., vol. 9, no. 1, pp. 161–174, Mar. 2008.
[3] Kanhere N. K., and Birchfield S. T., "Real-time incremental segmentation and tracking of vehicles at low camera angles using stable features", IEEE Trans. Intell. Transp. Syst., vol. 9, no. 1, pp. 148–160, Mar. 2008
[4] Vargas M., Milla J. M., Toral S. L., and Barrero F., "An enhanced background estimation algorithm for vehicle detection in urban traffic scenes", IEEE Trans. Veh. Technol., vol. 59, no. 8, pp. 3694–3709, Oct. 2010.
[5] Khairdoost N., Monadjemi A., and Jamshidi k., "Front and Rear Vehicle Detection Using Hypothesis Generation and Verification", Signal & Image Processing: An International Journal (SIPIJ) Vol.4, No.4, and August 2013.
[6] Li W., Liu P., Wang Y., Ni H., Wen C., and Fan J., "On-board Robust Vehicle Detection Using Knowledge-based Features and Motion Trajectory", International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 8, No. 2015.
[7] Tsai Y.  M., K. Huan Y., Tsai C. C., and Chen L. G.,  "An  exploration  of  on-road  vehicle  detection  using hierarchical  scaling  schemes", IEEE International  Conference  on  Image  Processing,  Hong  Kong,  (2010)    September.
[8] Niknejad  H. T., Takeuchi A., Mita S. and McAllester D., "On-Road Multivehicle Tracking Using Deformable Object Model and Particle Filter With Improved Likelihood Estimation",  IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, (2012).
[9] Southall B., Bansal M. and Eledath J., "Real-time Vehicle Detection for Highway Driving", IEEE Conference on Computer Vision and Pattern Recognition Miami, Florida, USA, (2009) June.
[10] Sivaraman S., and Trivedi M. M., "A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking", IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, (2010).
[11] Arrospide J., Salgado L., Nieto M., and Jaureguizar F., "On-board robust vehicle detection and tracking using adaptive  quality  evaluation",  IEEE  International  Conference  on Image  Processing,  San  Diego,  California, USA, ( 2008) October.
[12] Alonso D., Salgado L., Nieto M., "Robust Vehicle Detection Through Multidimensional Classification For on Board  Video  Based  Systems",  IEEE  International  Conference  on  Image  Processing,  San  Antonio,  Texas, USA, (2007) September.
[13] Li Y. L., Tian B., Yao Q., " Vehicle Detection Based on the and– or Graph for Congested Traffic Conditions", Intelligent Transportation Systems, IEEE Transactions on  (Volume:14 ,  Issue: 2 ) 15 March 2013.
[14] Nguyen V. D., Nguyen T. T., Nguyen D. D., Lee S. J.,  Jeon J. W., "A Fast Evolutionary Algorithm for Real-Time Vehicle Detection", Vehicular Technology, IEEE Transactions on  (Volume:62 ,  Issue: 6 ) 25 January 2013.
[15] Tian B., Li Y., Li B., Wen D., "Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance", Intelligent Transportation Systems, IEEE Transactions on  (Volume:15 ,  Issue: 2 )  April 2014.
[16] Wu B. F., Juang J. H., "Adaptive Vehicle Detector Approach for Complex Environments", IEEE Transactions on Intelligent Transportation Systems, VOL. 13, NO. 2, JUNE 2012.
[17] Lucas B. D., and Kanade T., "An Iterative Image Registration Technique with an Application to Stereo Vision", From Proceedings of Imaging Understanding Workshop, pp. 121-130 (1981).
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