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International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.4, No.9, Sep. 2012

Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey

Full Text (PDF, 272KB), PP.52-59


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Author(s)

Kanwal Yousaf,Arta Iftikhar,Ali Javed

Index Terms

Vehicle classification, Video based vehicle classification, Vehicle classification algorithms

Abstract

Vehicle classification has emerged as a significant field of study because of its importance in variety of applications like surveillance, security system, traffic congestion avoidance and accidents prevention etc. So far numerous algorithms have been implemented for classifying vehicle. Each algorithm follows different procedures for detecting vehicles from videos. By evaluating some of the commonly used techniques we highlighted most beneficial methodology for classifying vehicles. In this paper we pointed out the working of several video based vehicle classification algorithms and compare these algorithms on the basis of different performance metrics such as classifiers, classification methodology or principles and vehicle detection ratio etc. After comparing these parameters we concluded that Hybrid Dynamic Bayesian Network (HDBN) Classification algorithm is far better than the other algorithms due to its nature of estimating the simplest features of vehicles from different videos. HDBN detects vehicles by following important stages of feature extraction, selection and classification. It extracts the rear view information of vehicles rather than other information such as distance between the wheels and height of wheel etc.

Cite This Paper

Kanwal Yousaf,Arta Iftikhar,Ali Javed,"Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey", IJIGSP, vol.4, no.9, pp.52-59, 2012.

Reference

[1]Kostia Robert, “Night-Time Traffic Surveillance A Robust Framework for Multi-Vehicle Detection, Classification and Tracking”, Advanced Video and Signal Based Surveillance, IEEE, 2009

[2]Ryan P. Avery, Yinhai Wang, and G. Scott Rutherford,” Length-Based Vehicle Classification Using Images from Un-calibrated Video Cameras”, Intelligent Transportation Systems, 2004 Proceedings. The 7th International IEEE Conference

[3]Guohui Zhang, Ryan P. Avery, Yinhai Wang, “A Video-based Vehicle Detection and Classification System for Real-time Traffic Data Collection Using Uncalibrated Video Cameras”, National Research Council, Washington, DC , 2007

[4]Kun Wu, Haiying Zhang, Tianmao Xu, Ju Song, “Overview of Video-Based Vehicle Detection Technologies”, The 6th International Conference on Computer Science & Education (ICCSE 2011) August 3-5, 2011. SuperStar Virgo, Singapore

[5]Yang Wang,, “Real-Time Moving Vehicle Detection With Cast Shadow Removal in Video Based on Conditional Random Field”, IEEE transactions on circuits and systems for video technology, vol. 19, no. 3, March 2009

[6]Zhong Qin,” Method of Vehicle Classification Based on Video”, Proceedings of the 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics July 2 - 5, 2008, Xi'an, China

[7]Jin-Cyuan Lai, Shih-Shinh Huang, and Chien-Cheng Tseng , “Image-Based Vehicle Tracking and Classification on the Highway” Green Circuits and Systems (ICGCS), 2010 International Conference, 21-23 June 2010

[8]Jun-Wei Hsieh, Shih-Hao Yu, Yung-Sheng Chen, Wen-Fong Hu “Automatic Traffic Surveillance System for Vehicle Tracking and Classification”, IEEE Transactions On Intelligent Transportation Systems, Vol. 7, No. 2, June 2006

[9]Anshul Goyal and Brijesh Verma ,“ A Neural Network based Approach for the Vehicle Classification”, Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing (CIISP 2007)

[10]Wei Wang ,Yulong Shang ,Jinzhi Guo,Zhiwei Qian” Real-time Vehicle Classification Based on Eigenface”, 2011 IEEE

[11]Tao Xu, Hong Liu, Yueliang Qian and Han Zhang, “A Novel Method for People and Vehicle Classification Based on Hough Line Feature”, International Conference on Information Science and Technology March 26-28, 2011 Nanjing, Jiangsu, China

[12]Peijin Ji, Lianwen Jin, Xutao Li,” Vision-based Vehicle Type Classification Using Partial Gabor Filter Bank”, Proceedings of the IEEE International Conference on Automation and Logistics August 18 - 21, 2007, Jinan, China

[13]Mehran Kafai , Bir Bhanu “Dynamic Bayesian Networks for Vehicle Classification in Video”, IEEE Transactions On Industrial Informatics, Vol. 8, No. 1, February 2012.