Online Spatial-temporal Data Fusion for Robust Adaptive Tracking
One problem with the adaptive tracking is that the data that are used to train the new target model often contain errors and these errors will affect the quality of the new target model. As time passes by, these errors will accumulate and eventually lead the tracker to drift away. In this paper, we...
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| creator | Jixu Chen Qiang Ji |
| description | One problem with the adaptive tracking is that the data that are used to train the new target model often contain errors and these errors will affect the quality of the new target model. As time passes by, these errors will accumulate and eventually lead the tracker to drift away. In this paper, we propose a new method based on online data fusion to alleviate this tracking drift problem. Based on combining the spatial and temporal data through a dynamic Bayesian network, the proposed method can improve the quality of online data labeling, therefore minimizing the error associated with model updating and alleviating the tracking drift problem. Experiments show the proposed method significantly improves the performance of an existing adaptive tracking method. |
| doi_str_mv | 10.1109/CVPR.2007.383436 |
| format | Conference Proceeding |
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As time passes by, these errors will accumulate and eventually lead the tracker to drift away. In this paper, we propose a new method based on online data fusion to alleviate this tracking drift problem. Based on combining the spatial and temporal data through a dynamic Bayesian network, the proposed method can improve the quality of online data labeling, therefore minimizing the error associated with model updating and alleviating the tracking drift problem. 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As time passes by, these errors will accumulate and eventually lead the tracker to drift away. In this paper, we propose a new method based on online data fusion to alleviate this tracking drift problem. Based on combining the spatial and temporal data through a dynamic Bayesian network, the proposed method can improve the quality of online data labeling, therefore minimizing the error associated with model updating and alleviating the tracking drift problem. Experiments show the proposed method significantly improves the performance of an existing adaptive tracking method.</description><subject>Bayesian methods</subject><subject>Computer errors</subject><subject>Data engineering</subject><subject>Labeling</subject><subject>Lighting</subject><subject>Pollution measurement</subject><subject>Robustness</subject><subject>Systems engineering and theory</subject><subject>Target tracking</subject><subject>Time measurement</subject><issn>1063-6919</issn><isbn>9781424411795</isbn><isbn>1424411793</isbn><isbn>1424411807</isbn><isbn>9781424411801</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkF1LwzAYhSMqOGfvBW_yB1rzJmk-7hzVqTCYzOLteNsmEu3a0maC_96COzeHBw7PxSHkFlgGwOx98fG2yzhjOhNGSKHOyDVILiWAYfqcJFabE2ubX5AFMCVSZcFekWSavtgcM09zsyAP264NnaPvA8aAbRrdYehHbOkjRqTr4xT6jvp-pLu-Ok6RrhocYvhxtByx_g7d5w259NhOLjn1kpTrp7J4STfb59ditUmDZTHldcMbjo0SzpoauGO2Etxo9HmNrAFprZNYCQncAHhUOZOoufO5MuArFEty968Nzrn9MIYDjr97yTWT8wN_OFRLmQ</recordid><startdate>200706</startdate><enddate>200706</enddate><creator>Jixu Chen</creator><creator>Qiang Ji</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200706</creationdate><title>Online Spatial-temporal Data Fusion for Robust Adaptive Tracking</title><author>Jixu Chen ; Qiang Ji</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-2cd2d2ad63e98c12e09b3287af5ca0d1499e4ab3412811fa6504a72ef5681fba3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Bayesian methods</topic><topic>Computer errors</topic><topic>Data engineering</topic><topic>Labeling</topic><topic>Lighting</topic><topic>Pollution measurement</topic><topic>Robustness</topic><topic>Systems engineering and theory</topic><topic>Target tracking</topic><topic>Time measurement</topic><toplevel>online_resources</toplevel><creatorcontrib>Jixu Chen</creatorcontrib><creatorcontrib>Qiang Ji</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jixu Chen</au><au>Qiang Ji</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Online Spatial-temporal Data Fusion for Robust Adaptive Tracking</atitle><btitle>2007 IEEE Conference on Computer Vision and Pattern Recognition</btitle><stitle>CVPR</stitle><date>2007-06</date><risdate>2007</risdate><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>1063-6919</issn><isbn>9781424411795</isbn><isbn>1424411793</isbn><eisbn>1424411807</eisbn><eisbn>9781424411801</eisbn><abstract>One problem with the adaptive tracking is that the data that are used to train the new target model often contain errors and these errors will affect the quality of the new target model. As time passes by, these errors will accumulate and eventually lead the tracker to drift away. In this paper, we propose a new method based on online data fusion to alleviate this tracking drift problem. Based on combining the spatial and temporal data through a dynamic Bayesian network, the proposed method can improve the quality of online data labeling, therefore minimizing the error associated with model updating and alleviating the tracking drift problem. Experiments show the proposed method significantly improves the performance of an existing adaptive tracking method.</abstract><pub>IEEE</pub><doi>10.1109/CVPR.2007.383436</doi><tpages>8</tpages></addata></record> |
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| ispartof | 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007, p.1-8 |
| issn | 1063-6919 |
| language | eng |
| recordid | cdi_ieee_primary_4270434 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Computer errors Data engineering Labeling Lighting Pollution measurement Robustness Systems engineering and theory Target tracking Time measurement |
| title | Online Spatial-temporal Data Fusion for Robust Adaptive Tracking |
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