Open Access Open Access  Restricted Access Subscription or Fee Access

Vehicle Detection and Classification using Neural Network

Deepa Sajjanar, B. S. Rekha, Dr. G. N. Srinivasan


Nowadays, intelligent transportation system has become more important in traffic surveillance. The proposed implementation deals with vehicle detection and classification using the neural network. The current work is carried out in MATLAB. The images are preprocessed and then Segmentation is carried out.  Morphological operations are applied to get a vehicle in the image. The features of vehicle images are extracted to detect a class of vehicle. Additional 8 histogram count generated from the vehicle shape is used to characterize the vehicles in the images. These features are trained with the Artificial Neural Network (ANN). So that vehicle images are classified as image with bike or car based on the input i.e. testing and trained features. The average accuracy of the vehicle detected and classified is achieved with percentage of 80%.


Canny Edge Detection, Morphological Operations, Histogram Count, Neural Network

Full Text:



Piyush P, Rajeev Rajan, Leena Mary, Member, IEEE and Bino I. Koshy, “Vehicle Detection and Classification using Audio-Visual cues” 2016 IEEE

Prem Kumar Bhaskar, Suet-Peng Yong, “Image Processing Based Vehicle Detection and Tracking Method”, Universiti Teknologi PETRONAS Seri Iskandar, 31750, Perak, Malaysia 2014 IEEE

Prof. Suvarna Nandyal1, Mrs. Pushpalata Patil, “¬¬Vehicle Detection and Traffic Assessment Using Images” IJCSMC, Vol. 2, Issue. 9, 2013, pg.8 – 17

Aisha Ajmal and Ibrahim M. Hussain, “Morphological Process based Vehicle Detection and Classification”, MASAUM Journal of Basic and Applied Science Vol.1, No 2 September 2009

Raad Ahmed Hadi, Ghazali Sulong and Loay Edwar George, “Vehicle Detection and Tracking Techniques: A Concise Review”, Signal & Image Processing: An International Journal (SIPIJ) Vol.5, No.1, February 2014

Huang Guan1, Wang Xingang1, Wu Wenqi1, Zhou Han2, Wu Yuanyuan, “Real-Time Lane-Vehicle Detection and Tracking System” 2016 IEEE

K.V. Arya, Shailendra Tiwari, Saurabh Behwal, “Real-time Vehicle Detection and Tracking”, Multimedia & Information Security Research Group, 2016 IEEE

Isha Jain1 & Babita Rani, “Vehicle Detection Using Image Processing and Fuzzy Logic”, International Journal of Computer Science & Communication Vol. 1, No. 2, July-December 2010.

Jun Hu1, Wei Liu, Huai Yuan, Hong Zhao, "A Multi-View Vehicle Detection Method Based on Deep Neural Networks”, 2017 9th International Conference on Measuring Technology and Mechatronics Automation.

Bharti Sharma, Vinod Kumar Katiyar, Arvind Kumar Gupta, Akansha Singh, “The Automated Vehicle Detection of Highway Traffic Images by Differential Morphological Profile”, Journal of Transportation Technologies, 2014, 4, 150-156.

Can Nguyen Van, Cuong Nguyen Ngoc, “Vehicle Classification in Video Based on Shape Analysis”, 2014 UKSim-AMSS 8th European Modelling Symposium.

Feris. R.S, Siddiquie. B, Petterson. J, Yun Zhai, Datta. A Brown. L.M, Pankanti. S, "Large-Scale Vehicle Detection, Indexing, and Search in Urban Surveillance Videos," Multimedia, IEEE Transactions on, vol.14, no.1, pp.28, 42, Feb. 2012.

Bouvie. C, Scharcanski. J, Barcellos. P, Lopes Escouto. F, "Tracking and counting vehicles in traffic video sequences using particle filtering," Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International, vol., no., pp.812,815, 6-9 May 2013.

Deng-Yuan Huang, Chao-Ho Chen, Wu-Chih Hu, Shu-ChungYi, and Yu-Feng Lin; “Feature-Based Vehicle Flow Analysis and Measurement for a Real-Time Traffic Surveillance System”, Journal of Information Hiding and Multimedia Signal Processing, Volume 3, Number 3, pp. 2073 4212, July 2012.

M. Daigavane, and P. R. Bajaj, “Real Time Vehicle Detection and Counting Method for Unsupervised Traffic Video on Highways”, International Journal of Computer Science and Network Security, vol.10, no. 8, pp. 112-117, 2014.



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.