Neural Network Approach for the Identification System of the Type of Vehicle
This paper represents a framework for multi-class vehicle type identification based on several geometrical parameters. The system of identification of object must thus have a very great adaptability. We represent a system of identification of the type (model) of vehicles per vision. Several geometri...
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| creator | Daya, B Akoum, A H Chauvet, P |
| description | This paper represents a framework for multi-class vehicle type identification based on several geometrical parameters. The system of identification of object must thus have a very great adaptability. We represent a system of identification of the type (model) of vehicles per vision. Several geometrical parameters (distance, surface, ratio ...) of decision, on bases of images taken in real conditions, were tested and analyzed. The details of preprocessing as well as the features represented above are described in this paper. According to these parameters, the rate of identification can reach 95% on a basis of images made up of 9 classes of the type of vehicles. Then artificial neural network (ANNE) was used to verify and to classify the different types of the vehicles, and a ratio of identification of about 97% was obtained. The details of the implementation and results of the simulation are discussed in this paper. |
| doi_str_mv | 10.1109/CICN.2010.42 |
| format | Conference Proceeding |
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The system of identification of object must thus have a very great adaptability. We represent a system of identification of the type (model) of vehicles per vision. Several geometrical parameters (distance, surface, ratio ...) of decision, on bases of images taken in real conditions, were tested and analyzed. The details of preprocessing as well as the features represented above are described in this paper. According to these parameters, the rate of identification can reach 95% on a basis of images made up of 9 classes of the type of vehicles. Then artificial neural network (ANNE) was used to verify and to classify the different types of the vehicles, and a ratio of identification of about 97% was obtained. 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The details of the implementation and results of the simulation are discussed in this paper.</description><subject>Artificial neural networks</subject><subject>Classification algorithms</subject><subject>Finite impulse response filter</subject><subject>geometrical parameters</subject><subject>Multiclass Classification</subject><subject>Neural Networks</subject><subject>Three dimensional displays</subject><subject>Training</subject><subject>type of vehicle</subject><subject>Vehicles</subject><isbn>9781424486533</isbn><isbn>142448653X</isbn><isbn>0769542549</isbn><isbn>9780769542546</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjMtOhEAURNsYE3Vk585N_wBjP253c5cT4oOE4ELidgLNJbQyAwGM4e8dH7WpUzlJMXYrxVZKgfdplhZbJU4T1Bm7Fs6iAWUAz1mELpGgABJrtL5k0Ty_i1OMcqDwiuUFfU5Vzwtavobpg-_GcRoq3_F2mPjSEc8aOi6hDb5awnDkr-u80IEP7a8s15F--I264Hu6YRdt1c8U_feGlY8PZfoc5y9PWbrL44BiiZO6kRYlkvNaC0dYo9WgEgBft5KEsaDRK_La1VhJFI0Eb8kqk9SiBa837O7vNhDRfpzCoZrWvXFCorH6GwM4THA</recordid><startdate>201011</startdate><enddate>201011</enddate><creator>Daya, B</creator><creator>Akoum, A H</creator><creator>Chauvet, P</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201011</creationdate><title>Neural Network Approach for the Identification System of the Type of Vehicle</title><author>Daya, B ; Akoum, A H ; Chauvet, P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-8bd16919e7c3307e9b96342844cbf1e056439c2ec37b9a190d14c6e6258b0f4c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Artificial neural networks</topic><topic>Classification algorithms</topic><topic>Finite impulse response filter</topic><topic>geometrical parameters</topic><topic>Multiclass Classification</topic><topic>Neural Networks</topic><topic>Three dimensional displays</topic><topic>Training</topic><topic>type of vehicle</topic><topic>Vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Daya, B</creatorcontrib><creatorcontrib>Akoum, A H</creatorcontrib><creatorcontrib>Chauvet, P</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Daya, B</au><au>Akoum, A H</au><au>Chauvet, P</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Neural Network Approach for the Identification System of the Type of Vehicle</atitle><btitle>2010 International Conference on Computational Intelligence and Communication Networks</btitle><stitle>cicn</stitle><date>2010-11</date><risdate>2010</risdate><spage>162</spage><epage>166</epage><pages>162-166</pages><isbn>9781424486533</isbn><isbn>142448653X</isbn><eisbn>0769542549</eisbn><eisbn>9780769542546</eisbn><abstract>This paper represents a framework for multi-class vehicle type identification based on several geometrical parameters. 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| subjects | Artificial neural networks Classification algorithms Finite impulse response filter geometrical parameters Multiclass Classification Neural Networks Three dimensional displays Training type of vehicle Vehicles |
| title | Neural Network Approach for the Identification System of the Type of Vehicle |
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