Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision
A new image-based approach for fast and robust vehicle tracking from a moving platform is presented. Position, orientation, and full motion state, including velocity, acceleration, and yaw rate of a detected vehicle, are estimated from a tracked rigid 3-D point cloud. This point cloud represents a 3...
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| Veröffentlicht in: | IEEE Transactions on intelligent transportation systems 2009-12, Vol.10 (4), p.560-571 |
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| container_title | IEEE Transactions on intelligent transportation systems |
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| creator | Barth, A. Franke, U. |
| description | A new image-based approach for fast and robust vehicle tracking from a moving platform is presented. Position, orientation, and full motion state, including velocity, acceleration, and yaw rate of a detected vehicle, are estimated from a tracked rigid 3-D point cloud. This point cloud represents a 3-D object model and is computed by analyzing image sequences in both space and time, i.e., by fusion of stereo vision and tracked image features. Starting from an automated initial vehicle hypothesis, tracking is performed by means of an extended Kalman filter. The filter combines the knowledge about the movement of the rigid point cloud's points in the world with the dynamic model of a vehicle. Radar information is used to improve the image-based object detection at far distances. The proposed system is applied to predict the driving path of other traffic participants and currently runs at 25 Hz (640 times 480 images) on our demonstrator vehicle. |
| doi_str_mv | 10.1109/TITS.2009.2029643 |
| format | Article |
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Position, orientation, and full motion state, including velocity, acceleration, and yaw rate of a detected vehicle, are estimated from a tracked rigid 3-D point cloud. This point cloud represents a 3-D object model and is computed by analyzing image sequences in both space and time, i.e., by fusion of stereo vision and tracked image features. Starting from an automated initial vehicle hypothesis, tracking is performed by means of an extended Kalman filter. The filter combines the knowledge about the movement of the rigid point cloud's points in the world with the dynamic model of a vehicle. Radar information is used to improve the image-based object detection at far distances. The proposed system is applied to predict the driving path of other traffic participants and currently runs at 25 Hz (640 times 480 images) on our demonstrator vehicle.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2009.2029643</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>Piscataway, NJ: IEEE</publisher><subject>Acceleration ; Applied sciences ; Artificial intelligence ; Clouds ; Computer science; control theory; systems ; Control theory. Systems ; Driving ; Exact sciences and technology ; Ground, air and sea transportation, marine construction ; Image sequence analysis ; Kalman filtering ; Mathematical models ; Motion detection ; object detection ; Pattern recognition. Digital image processing. 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Position, orientation, and full motion state, including velocity, acceleration, and yaw rate of a detected vehicle, are estimated from a tracked rigid 3-D point cloud. This point cloud represents a 3-D object model and is computed by analyzing image sequences in both space and time, i.e., by fusion of stereo vision and tracked image features. Starting from an automated initial vehicle hypothesis, tracking is performed by means of an extended Kalman filter. The filter combines the knowledge about the movement of the rigid point cloud's points in the world with the dynamic model of a vehicle. Radar information is used to improve the image-based object detection at far distances. The proposed system is applied to predict the driving path of other traffic participants and currently runs at 25 Hz (640 times 480 images) on our demonstrator vehicle.</description><subject>Acceleration</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Clouds</subject><subject>Computer science; control theory; systems</subject><subject>Control theory. Systems</subject><subject>Driving</subject><subject>Exact sciences and technology</subject><subject>Ground, air and sea transportation, marine construction</subject><subject>Image sequence analysis</subject><subject>Kalman filtering</subject><subject>Mathematical models</subject><subject>Motion detection</subject><subject>object detection</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Platforms</subject><subject>Radar tracking</subject><subject>Robotics</subject><subject>Robustness</subject><subject>sensor data fusion</subject><subject>State estimation</subject><subject>Stereo vision</subject><subject>Tracking</subject><subject>Vehicle detection</subject><subject>Vehicle driving</subject><subject>Vehicles</subject><subject>Vision</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMFq3DAQhk1JoUnaByi9iEDoyemMZNnSMSSbNpCSQjY5FYSkHTUKtpVI3kLfvnZ3yaGXmWH0_YP4quojwhki6C_r6_XdGQfQc-G6bcSb6hClVDUAtgfLzJtag4R31VEpT_O2kYiH1c9VmeJgpzj-YtMjscscfy_z3WQnYimw29GnYdk80GP0PRV2ldPALPue_oE_ejuFlAd2X3Y5ypTYQywxje-rt8H2hT7s-3F1f7VaX3yrb26_Xl-c39ReyGaqsWkVNS1Jp4OCjRaeO0BOsgF0LpD0iE44J0W3UUgtKKdCIKU6AbDpnDiuPu_uPuf0sqUymSEWT31vR0rbYlQngaPo9Eye_Ec-pW0e588ZJZXQwFs-Q7iDfE6lZArmOc-O8h-DYBbbZrFtFttmb3vOnO4P2-JtH7IdfSyvQc45th3Imfu04yIRvT5LzjVXWvwF37KHXQ</recordid><startdate>20091201</startdate><enddate>20091201</enddate><creator>Barth, A.</creator><creator>Franke, U.</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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Systems</topic><topic>Driving</topic><topic>Exact sciences and technology</topic><topic>Ground, air and sea transportation, marine construction</topic><topic>Image sequence analysis</topic><topic>Kalman filtering</topic><topic>Mathematical models</topic><topic>Motion detection</topic><topic>object detection</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Platforms</topic><topic>Radar tracking</topic><topic>Robotics</topic><topic>Robustness</topic><topic>sensor data fusion</topic><topic>State estimation</topic><topic>Stereo vision</topic><topic>Tracking</topic><topic>Vehicle detection</topic><topic>Vehicle driving</topic><topic>Vehicles</topic><topic>Vision</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Barth, A.</creatorcontrib><creatorcontrib>Franke, U.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE Transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Barth, A.</au><au>Franke, U.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision</atitle><jtitle>IEEE Transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2009-12-01</date><risdate>2009</risdate><volume>10</volume><issue>4</issue><spage>560</spage><epage>571</epage><pages>560-571</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>A new image-based approach for fast and robust vehicle tracking from a moving platform is presented. Position, orientation, and full motion state, including velocity, acceleration, and yaw rate of a detected vehicle, are estimated from a tracked rigid 3-D point cloud. This point cloud represents a 3-D object model and is computed by analyzing image sequences in both space and time, i.e., by fusion of stereo vision and tracked image features. Starting from an automated initial vehicle hypothesis, tracking is performed by means of an extended Kalman filter. The filter combines the knowledge about the movement of the rigid point cloud's points in the world with the dynamic model of a vehicle. Radar information is used to improve the image-based object detection at far distances. The proposed system is applied to predict the driving path of other traffic participants and currently runs at 25 Hz (640 times 480 images) on our demonstrator vehicle.</abstract><cop>Piscataway, NJ</cop><pub>IEEE</pub><doi>10.1109/TITS.2009.2029643</doi><tpages>12</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) |
| subjects | Acceleration Applied sciences Artificial intelligence Clouds Computer science control theory systems Control theory. Systems Driving Exact sciences and technology Ground, air and sea transportation, marine construction Image sequence analysis Kalman filtering Mathematical models Motion detection object detection Pattern recognition. Digital image processing. Computational geometry Platforms Radar tracking Robotics Robustness sensor data fusion State estimation Stereo vision Tracking Vehicle detection Vehicle driving Vehicles Vision |
| title | Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision |
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