A SVM Based Approach for Real Time Detection and Classification of Vehicles at the Toll Gates Using Video Sequences

Document Type : Original Article


Electrical Engineering Department, Shahrood University, Shahrood, Semnan, Iran


This paper aims to present a real-time scheme for detection and classification of vehicles passing the toll gates in Iran. In our approach, a set of videos are captured using a stationary camera, placed on the roadside, at a little distance from the toll booth. The algorithm is designed in a way that there is no need for camera calibration. Based on our videos, 3 ROIs are defined, two of them are considered to determine if a vehicle is passing and the other one is the region containing the vehicle. This work starts with the training phase, in which, for each image in a manually gathered database, HOG vectors are extracted. Two SVMs are trained in this phase, one for distinguishing vehicles from non-vehicles, and one for classifying vehicles into light and heavy vehicles. After finishing the training, in the testing phase, firstly, foreground mask is obtained differencing two consecutive frames of the video. Then, those two aforementioned ROIs are checked in every frame and as soon as a vehicle is inside the interest region, that ROI is captured. Next, the captured frame is passed to the first SVM and it is classified as vehicle or non-vehicle. Those which are identified as vehicles are passed to the second SVM to be classified as light or heavy vehicle. Average true-positive and precision rates of the vehicle detection step are 92.5% and 97.5% respectively and the same rates, for the recognition step, are 98% and 0.99%.

Rahmani-Seryasat, O., Haddadnia, J., & Ghayoumi-Zadeh, H. (2015). A new method to classify breast cancer tumors and their fractionation. Ciência e Natura37, 51. doi:10.5902/2179460x19428
Rahmani Seryasat, O., Haddadnia, J., & Zadeh, H. (2016). Assessment of a novel computer aided mass diagnosis system in mammograms. Iranian Quarterly Journal of Breast Disease9(3), 31–41.
Seryasat, O. R., & Haddadnia, J. (2017). Assessment of a novel computer aided mass diagnosis system in mammograms. Biomedical Research28(7), 3129–3135.
Haddadnia, J., Seryasat, O. R., & Rabiee, H. (2013). Thyroid diseases diagnosis using probabilistic neural network and principal component analysis. Journal of Basic and Applied Science Research3(2), 593–598.
Zhang, B., & Zhou, Y. (2012, Οκτώβριος). Reliable vehicle type classification by Classified Vector Quantization. 2012 5th International Congress on Image and Signal Processing (CISP), Chongqing, Sichuan, China. doi:10.1109/cisp.2012.6469857.
Youpan, H., Qing, H., Xiaobin, Z., Haibin, W., Baopu, L., & Zhenfu, W. (2013). Algorithm for vision-based vehicle detection and classification. Στο Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on (pp. 568–572).
Mita, T., Kaneko, T., & Hori, O. (2005). Joint Haar-like features for face detection. Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1. Beijing, China. doi:10.1109/iccv.2005.129
Hua, H., Qian, Z., Yulan, J., & Shuming, T. (2008). A 2DLDA Based Algorithm for Real Time Vehicle Type Recognition. Στο 11th International IEEE Conference on (pp. 298–303).
Yu, P., Jin, J. S., Suhuai, L., Min, X., & Yue, C. (2012). Vehicle Type Classification Using PCA with Self- Clustering. 2012 IEEE International Conference on (pp. 384–389). ICMEW.
Bao, T., Nam, G. H., & Byung Ryong, L. (2009). Vehicle detection and recognition for automated guided vehicle. ICCAS-SICE (pp. 671–676).
Jun-Wei, H., Li-Chih, C., Duan-Yu, C., & Shyi-Chyi, C. (2013). Vehicle make and model recognition using symmetrical SURF. Advanced Video and Signal Based Surveillance (AVSS) (pp. 472–477).