Efficient image gradient based vehicle localization
This paper reports novel algorithms for the efficient localization and recognition of traffic in traffic scenes. The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correl...
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| Veröffentlicht in: | IEEE transactions on image processing 2000-08, Vol.9 (8), p.1343-1356 |
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| container_title | IEEE transactions on image processing |
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| creator | Tan, T.N. Baker, K.D. |
| description | This paper reports novel algorithms for the efficient localization and recognition of traffic in traffic scenes. The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correlations based directly on image gradient data, which can be computed "on the fly." The algorithms are therefore very well suited to real-time implementation. The algorithms make use of two a priori sources of knowledge about the scene and the objects expected: (1) the ground-plane constraint and (2) the fact that the overall shape of road vehicles is strongly rectilinear. Additional efficiency is derived from making the weak perspective assumption. These assumptions are valid in the road traffic application domain. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles in several traffic scenes demonstrates the efficiency and robustness of context-based image understanding in road traffic scene analysis. The limitations of the algorithms are also addressed. |
| doi_str_mv | 10.1109/83.855430 |
| format | Article |
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The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correlations based directly on image gradient data, which can be computed "on the fly." The algorithms are therefore very well suited to real-time implementation. The algorithms make use of two a priori sources of knowledge about the scene and the objects expected: (1) the ground-plane constraint and (2) the fact that the overall shape of road vehicles is strongly rectilinear. Additional efficiency is derived from making the weak perspective assumption. These assumptions are valid in the road traffic application domain. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles in several traffic scenes demonstrates the efficiency and robustness of context-based image understanding in road traffic scene analysis. 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Computational geometry ; Physics ; Position (location) ; Road vehicles ; Roads ; Shape ; Signal processing ; Telecommunications and information theory ; Traffic control ; Traffic engineering ; Traffic flow ; Vehicles ; Voting</subject><ispartof>IEEE transactions on image processing, 2000-08, Vol.9 (8), p.1343-1356</ispartof><rights>2000 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correlations based directly on image gradient data, which can be computed "on the fly." The algorithms are therefore very well suited to real-time implementation. The algorithms make use of two a priori sources of knowledge about the scene and the objects expected: (1) the ground-plane constraint and (2) the fact that the overall shape of road vehicles is strongly rectilinear. Additional efficiency is derived from making the weak perspective assumption. These assumptions are valid in the road traffic application domain. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles in several traffic scenes demonstrates the efficiency and robustness of context-based image understanding in road traffic scene analysis. The limitations of the algorithms are also addressed.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Computer vision</subject><subject>Computers in experimental physics</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Image analysis</subject><subject>Image processing</subject><subject>Image recognition</subject><subject>Information, signal and communications theory</subject><subject>Instruments, apparatus, components and techniques common to several branches of physics and astronomy</subject><subject>Layout</subject><subject>Localization</subject><subject>Object recognition</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Physics</subject><subject>Position (location)</subject><subject>Road vehicles</subject><subject>Roads</subject><subject>Shape</subject><subject>Signal processing</subject><subject>Telecommunications and information theory</subject><subject>Traffic control</subject><subject>Traffic engineering</subject><subject>Traffic flow</subject><subject>Vehicles</subject><subject>Voting</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0c1LwzAYBvAgipvTg1cPUkQUD51589XkKGN-wMDL7iVtk5nRtbNZBf3rzWxx4MGRQxLyIw-8D0LngMcAWN1LOpacM4oP0BAUgxhjRg7DGfMkToCpATrxfokxMA7iGA1AEkFUQoaITq11uTPVJnIrvTDRotHFzzXT3hTRh3lzeWmiss516b70xtXVKTqyuvTmrN9HaP44nU-e49nr08vkYRbnIWYTK0sowzLhgAtQXIAIiVwIUDa3BDIVVlYUREPBDdNYUGuBWGo4FVICHaHb7tt1U7-3xm_SlfO5KUtdmbr1qQImmJKU7JUJZYSHkdAgb_6VRBLJBGX7YcIJCKECvPoDl3XbVGEuqZRMioQLGdBdh_Km9r4xNl03YdzNZwo43VaYSpp2FQZ72X_YZitT7GTfWQDXPdA-lGIbXeXO7xxjgsM286Jjzhjz-9qHfAPTp6b7</recordid><startdate>20000801</startdate><enddate>20000801</enddate><creator>Tan, T.N.</creator><creator>Baker, K.D.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Digital image processing. Computational geometry</topic><topic>Physics</topic><topic>Position (location)</topic><topic>Road vehicles</topic><topic>Roads</topic><topic>Shape</topic><topic>Signal processing</topic><topic>Telecommunications and information theory</topic><topic>Traffic control</topic><topic>Traffic engineering</topic><topic>Traffic flow</topic><topic>Vehicles</topic><topic>Voting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tan, T.N.</creatorcontrib><creatorcontrib>Baker, K.D.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tan, T.N.</au><au>Baker, K.D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient image gradient based vehicle localization</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2000-08-01</date><risdate>2000</risdate><volume>9</volume><issue>8</issue><spage>1343</spage><epage>1356</epage><pages>1343-1356</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>This paper reports novel algorithms for the efficient localization and recognition of traffic in traffic scenes. The algorithms eliminate the need for explicit symbolic feature extraction and matching. The pose and class of an object is determined by a form of voting and one-dimensional (1-D) correlations based directly on image gradient data, which can be computed "on the fly." The algorithms are therefore very well suited to real-time implementation. The algorithms make use of two a priori sources of knowledge about the scene and the objects expected: (1) the ground-plane constraint and (2) the fact that the overall shape of road vehicles is strongly rectilinear. Additional efficiency is derived from making the weak perspective assumption. These assumptions are valid in the road traffic application domain. The algorithms are demonstrated and tested using routine outdoor traffic images. Success with a variety of vehicles in several traffic scenes demonstrates the efficiency and robustness of context-based image understanding in road traffic scene analysis. 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| issn | 1057-7149 1941-0042 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Applied sciences Artificial intelligence Computer science control theory systems Computer vision Computers in experimental physics Exact sciences and technology Feature extraction Image analysis Image processing Image recognition Information, signal and communications theory Instruments, apparatus, components and techniques common to several branches of physics and astronomy Layout Localization Object recognition Pattern recognition. Digital image processing. Computational geometry Physics Position (location) Road vehicles Roads Shape Signal processing Telecommunications and information theory Traffic control Traffic engineering Traffic flow Vehicles Voting |
| title | Efficient image gradient based vehicle localization |
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