Adaptive genetic MM-CPHD filter for multitarget tracking
Multitarget tracking is an important topic in visual surveillance system. Considering imperfections of the cardinalized probability hypothesis density (CPHD) filter and the target maneuvers, we propose an adaptive genetic multiple-model CPHD filter in this paper. First, we discuss the filtering proc...
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| Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2017-08, Vol.21 (16), p.4755-4767 |
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| creator | Li, Bo Zhao, Jianli Pang, Fuwen |
| description | Multitarget tracking is an important topic in visual surveillance system. Considering imperfections of the cardinalized probability hypothesis density (CPHD) filter and the target maneuvers, we propose an adaptive genetic multiple-model CPHD filter in this paper. First, we discuss the filtering process and combined the standard CPHD filter with the multiple-model-based framework. Afterward, the sequential Monte Carlo implementation of the proposed filter for the nonlinear and non-Gaussian state estimates is presented in detail. To enhance the tracking performance as target start to maneuver, the adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles. On the other hand, the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets. The simulation results are provided to illustrate the reliability and efficiency of the proposed filter. |
| doi_str_mv | 10.1007/s00500-016-2087-0 |
| format | Article |
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Considering imperfections of the cardinalized probability hypothesis density (CPHD) filter and the target maneuvers, we propose an adaptive genetic multiple-model CPHD filter in this paper. First, we discuss the filtering process and combined the standard CPHD filter with the multiple-model-based framework. Afterward, the sequential Monte Carlo implementation of the proposed filter for the nonlinear and non-Gaussian state estimates is presented in detail. To enhance the tracking performance as target start to maneuver, the adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles. On the other hand, the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets. 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The simulation results are provided to illustrate the reliability and efficiency of the proposed filter.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Engineering</subject><subject>Estimates</subject><subject>False alarms</subject><subject>Genetic algorithms</subject><subject>Hypotheses</subject><subject>Maneuvers</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Methodologies and Application</subject><subject>Multiple target tracking</subject><subject>Robotics</subject><subject>State estimation</subject><subject>Surveillance systems</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kLFOwzAURS0EEqXwAWyRmA3PsR07Y1WgRWoFA8yW4zxHKW1SbBeJvyclSExM7w733CcdQq4Z3DIAdRcBJAAFVtActKJwQiZMcE6VUOXpT86pKgQ_JxcxbgBypiSfED2r7T61n5g12GFqXbZe0_nL8j7z7TZhyHwfst1hm9pkQ4MpS8G697ZrLsmZt9uIV793St4eH17nS7p6XjzNZyvqOCsSlULXKKAqKtCiVrJGhb4qmXQcNLOS8xJdroA5VN5WQpRMSCxEjYVXoJBPyc24uw_9xwFjMpv-ELrhpclLpkqhmZZDi40tF_oYA3qzD-3Ohi_DwBwFmVGQGQSZoyADA5OPTBy6XYPhb_l_6Bv4AGax</recordid><startdate>20170801</startdate><enddate>20170801</enddate><creator>Li, Bo</creator><creator>Zhao, Jianli</creator><creator>Pang, Fuwen</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20170801</creationdate><title>Adaptive genetic MM-CPHD filter for multitarget tracking</title><author>Li, Bo ; Zhao, Jianli ; Pang, Fuwen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-548de40b6b084d75de7efb915c3081a5339ec2701ce7fab449145e64de6f707e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Engineering</topic><topic>Estimates</topic><topic>False alarms</topic><topic>Genetic algorithms</topic><topic>Hypotheses</topic><topic>Maneuvers</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Methodologies and Application</topic><topic>Multiple target tracking</topic><topic>Robotics</topic><topic>State estimation</topic><topic>Surveillance systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Bo</creatorcontrib><creatorcontrib>Zhao, Jianli</creatorcontrib><creatorcontrib>Pang, Fuwen</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Bo</au><au>Zhao, Jianli</au><au>Pang, Fuwen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive genetic MM-CPHD filter for multitarget tracking</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2017-08-01</date><risdate>2017</risdate><volume>21</volume><issue>16</issue><spage>4755</spage><epage>4767</epage><pages>4755-4767</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>Multitarget tracking is an important topic in visual surveillance system. Considering imperfections of the cardinalized probability hypothesis density (CPHD) filter and the target maneuvers, we propose an adaptive genetic multiple-model CPHD filter in this paper. First, we discuss the filtering process and combined the standard CPHD filter with the multiple-model-based framework. Afterward, the sequential Monte Carlo implementation of the proposed filter for the nonlinear and non-Gaussian state estimates is presented in detail. To enhance the tracking performance as target start to maneuver, the adaptive genetic algorithm is used to improve the target state estimation accuracy at the time of state switching with the excellent particles. On the other hand, the undetected component of the measurement-updated weight of survival particle is compensated by the excess weight of newborn particle to correct the number estimates of targets. The simulation results are provided to illustrate the reliability and efficiency of the proposed filter.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-016-2087-0</doi><tpages>13</tpages></addata></record> |
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| subjects | Adaptive algorithms Algorithms Artificial Intelligence Computational Intelligence Control Engineering Estimates False alarms Genetic algorithms Hypotheses Maneuvers Mathematical Logic and Foundations Mechatronics Methodologies and Application Multiple target tracking Robotics State estimation Surveillance systems |
| title | Adaptive genetic MM-CPHD filter for multitarget tracking |
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