An overview of the type of vehicle detection techniques

  • Mojtaba Nasehi Student of Mobarakeh-Majlisi Branch of Azad University
  • Mohsen Ashourian
  • Payman Moallem
Keywords: convolution, vehicle, Neural Network

Abstract

Today, large-scale vehicles are scattered in different parts of the city and therefore need to be controlled by programmed systems. Applications of these systems include traffic control, urban planning, driverless vehicles, parking lot management by announcing the arrival of a vehicle, detecting stolen or offending vehicles, and so on. Due to challenges such as the multiplicity of objects in the image, weather conditions, different colors and designs of the type of vehicles and very diverse images from different angles of a vehicle in the section identifying the type of vehicles in the photo, Films, moving images, etc. have led to a variety of research, and in this article we will examine some of the techniques.

References

[1] B.D. Stewart, I. Reading, M.S. Thomson, T.D. Binnie, K.W. Dickinson, C.L. Wan, (1994). Adaptive lane finding in road traffic image analysis, Proceedings of Seventh International Conference on Road Traffic Monitoring and Control, IEE, London.
[2]W. Enkelmann, (1990). Obstacle detection by evaluation of optical flow field from image sequences, Proceedings of European Conference on Computer Vision, Antibes, France 427. 134–138.
[3]Y. Park, (2001), Shape-resolving local thresholding for object detection, Pattern Recognition Letters 22. 883–890.
[4] J.M. Blosseville, C. Krafft, F. Lenoir, V. Motyka, S. Beucher, (1994). New traffic measurements by image processing, IFAC Transportation systems, Tianjin, Proceedings.
[5]Y. Won, J. Nam, B.-H. Lee, (2001). Image pattern recognition in natural environment using morphological feature extraction, in: F.J. Ferri (Ed.), SSPR&SPR 2000, Springer, Berlin, pp.806–815.
[6]Zhigang, Zhou , Huan, Lei , Pengcheng, Ding , Guangbing, Zhou , Nan, Wang ,Wei-Kun, Zhou,( 2018). Vehicle target detection based on R-FCN, 2018 Chinese Control And Decision Conference (CCDC)
[7]Zhang, Zhaojin , Xu, Cunlu , Feng, Wei.(2016), Road vehicle detection and classification based on deep neural network. 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)
[8]Yu, Shaoyong , Wu, Yun , Li, Wei , Song, Zhijun , Zeng, Wenhua.(2017). A model for fine-grained vehicle classification based on deep learning, Neurocomputing
[9]Wang, Xinchen , Zhang, Weiwei , Wu, Xuncheng , Xiao, Lingyun , Qian, Yubin , Fang, Zhi.(2019). Real-time vehicle type classification with deep convolutional neural networks, Journal of Real-Time Image Processing
[10]Suhao, Li , Jinzhao, Lin , Guoquan, Li , Tong, Bai , Huiqian, Wang ,Yu, Pang.(2018). Vehicle type detection based on deep learning in traffic scene, Procedia computer science
[11]Sheng, Minglan , Liu, Chunfang , Zhang, Qi , Lou, Lu , Zheng, Yu.(2018) Vehicle Detection and Classification Using Convolutional Neural Networks, 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS)
[12]Murali, Anju, Nair, Bhavana B, Rao, Sethuraman N(2018). Comparative Study of Different CNNs for Vehicle Classification, 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)
[13]Li, Yinghua , Song, Bin . Kang, Xu . Du, Xiaojiang . Guizani, Mohsen.(2018). Vehicle-type detection based on compressed sensing and deep learning in vehicular networks,Sensors
[14]Kamran, Farrukh . Shahzad, Muhammad . Shafait, Faisal(2018). Automated military vehicle detection from low-altitude aerial images, 2018 Digital Image Computing: Techniques and Applications (DICTA)
[15]Hicham, Bensedik . Ahmed, Azough . Mohammed, Meknasssi(2018). Vehicle Type Classification Using Convolutional Neural Network, 2018 IEEE 5th International Congress on Information Science and Technology (CiSt)
[16]Ali, Mohamed Ashraf . El Munim, Hossam E Abd . Yousef, Ahmed Hassan . Hammad, Sherif(2018). A Deep Learning Approach for Vehicle Detection, 2018 13th International Conference on Computer Engineering and Systems (ICCES)
[17]X. Li, Z.-Q. Liu, K.-M. Leung, (2002). Detection of vehicles from traffic scenes using fuzzy integrals, Pattern Recognition 35. 967–980.
[18]H. Moon, R. Chellapa, A. Rosenfeld, (2003). Performance analysis of a simple vehicle detection algorithm, Image and Vision Computing 20. 1–13.
[19]G.D. Sullivan, K.D. Baker, A.D. Worrall, C.I. Attwood, P. M. Remagnino, (2004) Model-based vehicle detection and classification using orthographic approximations, Image and Vision Computing 15.
Published
2020-09-11
How to Cite
Nasehi, M., Ashourian, M., & Moallem, P. (2020). An overview of the type of vehicle detection techniques. Majlesi Journal of Telecommunication Devices, 9(3). Retrieved from http://journals.iaumajlesi.ac.ir/td/index/index.php/td/article/view/627
Section
Articles