© The Institution of Engineering and Technology
Intelligent transportation systems have received a lot of attention in the last decades. Vehicle detection is the key task in this area and vehicle counting and classification are two important applications. In this study, the authors proposed a vehicle detection method which selects vehicles using an active basis model and verifies them according to their reflection symmetry. Then, they count and classify them by extracting two features: vehicle length in the corresponding time-spatial image and the correlation computed from the grey-level co-occurrence matrix of the vehicle image within its bounding box. A random forest is trained to classify vehicles into three categories: small (e.g. car), medium (e.g. van) and large (e.g. bus and truck). The proposed method is evaluated using a dataset including seven video streams which contain common highway challenges such as different lighting conditions, various weather conditions, camera vibration and image blurring. Experimental results show the good performance of the proposed method and its efficiency for use in traffic monitoring systems during the day (in the presence of shadows), night and all seasons of the year.
References
-
-
1)
-
7. Ghasemi, A., Safabakhsh, R.: ‘A real-time multiple vehicle classification and tracking system with occlusion handling’. IEEE Int. Conf. on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, August 2012, pp. 109–115.
-
2)
-
21. Li, Y., Tian, B., Li, B., et al: ‘Vehicle detection with a part-based model for complex traffic conditions’. IEEE Int. Conf. on Vehicular Electronics and Safety (ICVES), Dongguan, July 2013, pp. 110–113.
-
3)
-
13. Gangodkar, D., Kumar, P., Mittal, A.: ‘Robust segmentation of moving vehicles under complex outdoor conditions’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (4), pp. 1738–1752 (doi: 10.1109/TITS.2012.2206076).
-
4)
-
19. Li, Y., Wang, F.Y., Li, B., et al: ‘A multi-scale model integrating multiple features for vehicle detection’. 16th Int. IEEE Conf. on Intelligent Transportation Systems-(ITSC), The Hague, October 2013, pp. 399–403.
-
5)
-
16. Sutar, V.B., Admuthe, M.L.: ‘Night time vehicle detection and classification using support vector machine’, IOSR J. VLSI Signal Process. (IOSR-JVSP), 2012, 1, (4), pp. 1–9 (doi: 10.9790/4200-0140109).
-
6)
-
2. Atiq, H.M., Farooq, U., Ibrahim, R., et al: ‘Vehicle detection and shape recognition using optical sensors: a review’. Second Int. Conf. on Machine Learning and Computing (ICMLC), Bangalore, February 2010, pp. 223–227.
-
7)
-
3. Liu, Y., Tian, B., Chen, S., et al: ‘A survey of vision-based vehicle detection and tracking techniques in ITS’. IEEE Int. Conf. on Vehicular Electronics and Safety (ICVES), Dongguan, July 2013, pp. 72–77.
-
8)
-
18. Leon, L.C., Hirata, R.: ‘Vehicle detection using mixture of deformable parts models: Static and dynamic camera’. 25th SIBGRAPI Conf. on Graphics, Patterns and Images (SIBGRAPI), Ouro Preto, August 2012, pp. 237–244.
-
9)
-
15. Jia, Y., Zhang, C.: ‘Front-view vehicle detection by Markov chain Monte Carlo method’, Pattern Recognit., 2009, 42, (3), pp. 313–321 (doi: 10.1016/j.patcog.2008.07.015).
-
10)
-
9. Unzueta, L., Nieto, M., Cortés, A., et al: ‘Adaptive multicue background subtraction for robust vehicle counting and classification’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (2), pp. 527–540 (doi: 10.1109/TITS.2011.2174358).
-
11)
-
13. Tsai, L., Hsieh, J., Fan, K.: ‘Vehicle detection using normalized color and edge map’, IEEE Trans. Image Process., 2007, 16, (3), pp. 850–864 (doi: 10.1109/TIP.2007.891147).
-
12)
-
20. Li, Y., Li, B., Tian, B., et al: ‘Vehicle detection based on the AND–OR graph for congested traffic conditions’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (2), pp. 984–993 (doi: 10.1109/TITS.2013.2250501).
-
13)
-
17. Chen, Y.L., Wu, B.F., Huang, H.Y.: ‘A real-time vision system for night time vehicle detection and traffic surveillance’, IEEE Trans. Ind. Electron., 2011, 58, (5), pp. 2030–2044 (doi: 10.1109/TIE.2010.2055771).
-
14)
-
26. Huang, D.Y., Chen, C.H., Hu, W.C., et al: ‘Feature-based vehicle flow analysis and measurement for a real-time traffic surveillance system’, J. Inf. Hiding Multimedia Signal Process., 2012, 3, (3), pp. 279–294.
-
15)
-
30. Rashid, N.U., Mithun, N.C., Joy, B.R., et al: ‘Detection and classification of vehicles from a video using time-spatial image’. Int. Conf. on Electrical and Computer Engineering (ICECE), Dhaka, December 2010, pp. 502–505.
-
16)
-
10. Pang, C.C.C., Lam, W.W.L., Yung, N.H.C.: ‘A method for vehicle count in the presence of multiple-vehicle occlusions in traffic images’, IEEE Trans. Intell. Transp., 2007, 8, (3), pp. 441–459 (doi: 10.1109/TITS.2007.902647).
-
17)
-
25. Li, S., Yu, H., Zhang, J., et al: ‘Video-based traffic data collection system for multiple vehicle types’, IET Intell. Transp. Syst., 2013, 8, (2), pp. 164–174 (doi: 10.1049/iet-its.2012.0099).
-
18)
-
11. Zhou, J., Gao, D., Zhang, D.: ‘Moving vehicle detection for automatic traffic monitoring’, IEEE Trans. Veh. Technol., 2007, 56, (1), pp. 51–59 (doi: 10.1109/TVT.2006.883735).
-
19)
-
1. Wu, K., Xu, T., Zhang, H.: ‘Overview of video-based vehicle detection technologies’. 6th Int. Conf. on Computer Science & Education (ICCSE), August 2011, pp. 821–825.
-
20)
-
24. Chiu, C.C., Ku, M.Y., Wang, C.Y.: ‘Automatic traffic surveillance system for vision-based vehicle recognition and tracking’, J. Inf. Sci. Eng., 2012, 26, (2), pp. 611–629.
-
21)
-
22. Yao, Y., Xiong, G., Wang, K., et al: ‘Vehicle detection method based on active basis model and symmetry in ITS’. 16th Int. IEEE Conf. on Intelligent Transportation Systems-(ITSC), The Hague, October 2013, pp. 614–618.
-
22)
-
28. Yang, M.T., Jhang, R.K., Hou, J.S.: ‘Traffic flow estimation and vehicle-type classification using vision-based spatial–temporal profile analysis’, IET Comput. Vis., 2013, 7, (5), pp. 394–404 (doi: 10.1049/iet-cvi.2012.0185).
-
23)
-
12. Wu, B.F., Juang, J.H.: ‘Adaptive vehicle detector approach for complex environments’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (2), pp. 817–827 (doi: 10.1109/TITS.2011.2181366).
-
24)
-
8. Mandellos, N.A., Keramitsoglou, I., Kiranoudis, C.T.: ‘A background subtraction algorithm for detecting and tracking vehicles’, Expert Syst. Appl., 2011, 38, (3), pp. 1619–1631 (doi: 10.1016/j.eswa.2010.07.083).
-
25)
-
27. Mithun, N.C., Rashid, N.U., Rahman, S.M.: ‘Detection and classification of vehicles from video using multiple time-spatial images’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (3), pp. 1215–1225 (doi: 10.1109/TITS.2012.2186128).
-
26)
-
14. Chen, Z., Ellis, T., Velastin, S.A.: ‘Vehicle detection, tracking and classification in urban traffic’. 15th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), Anchorage, September 2012, pp. 951–956.
-
27)
-
4. Jalali Moghaddam, M., Hosseini, M., Safabakhsh, R.: ‘Traffic light control based on Fuzzy Q_learning’. Int. Symp. on Artificial Intelligence and Signal Processing (AISP), Mashhad, March 2015, pp. 124–128.
-
28)
-
23. Li, Y., Yao, Q.: ‘Rear lamp based vehicle detection and tracking for complex traffic conditions’. 9th IEEE Int. Conf. on Networking, Sensing and Control (ICNSC), Beijing, April 2012, pp. 387–392.
-
29)
-
29. Wu, Y.N., Si, Z., Gong, H., et al: ‘Learning active basis model for object detection and recognition’, Int. J. Comput. Vis., 2010, 90, (2), pp. 198–235 (doi: 10.1007/s11263-009-0287-0).
-
30)
-
5. Kalaki, A.S., Safabakhsh, R.: ‘Current and adjacent lanes detection for an autonomous vehicle to facilitate obstacle avoidance using a monocular camera’. Iranian Conf. on Intelligent Systems (ICIS), Bam, February 2014, pp. 1–6.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2015.0157
Related content
content/journals/10.1049/iet-its.2015.0157
pub_keyword,iet_inspecKeyword,pub_concept
6
6