Simultaneous estimation of segmentation and shape
The main focus of this work is the integration of feature grouping and model based segmentation into one consistent framework. The algorithm is based on partitioning a given set of image features using a likelihood function that is parameterized on the shape and location of potential individuals in...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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2005
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| _version_ | 1797050206550753280 |
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| author | Rittscher, J Tu, P Krahnstoever, N |
| author_facet | Rittscher, J Tu, P Krahnstoever, N |
| author_sort | Rittscher, J |
| collection | OXFORD |
| description | The main focus of this work is the integration of feature grouping and model based segmentation into one consistent framework. The algorithm is based on partitioning a given set of image features using a likelihood function that is parameterized on the shape and location of potential individuals in the scene, using a variant of the EM formulation, maximum likelihood estimates of both the model parameters and the grouping are obtained simultaneously. The resulting algorithm performs global optimization and generates accurate results even when decisions can not be made using local context alone. An important feature of the algorithm is that the number of people in the scene is not modeled explicitly. As a result no prior knowledge or assumed distributions are required. The approach is shown to be robust with respect to partial occlusion, shadows, clutter, and can operate over a large range of challenging view angles including those that are parallel to the ground plane. Comparisons with existing crowd segmentation systems are made and the utility of coupling crowd segmentation with a temporal tracking system is demonstrated. © 2005 IEEE. |
| first_indexed | 2024-03-06T18:01:44Z |
| format | Journal article |
| id | oxford-uuid:00049faf-077c-4775-9175-38abb49e99b3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:01:44Z |
| publishDate | 2005 |
| record_format | dspace |
| spelling | oxford-uuid:00049faf-077c-4775-9175-38abb49e99b32022-03-26T08:27:10ZSimultaneous estimation of segmentation and shapeJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:00049faf-077c-4775-9175-38abb49e99b3EnglishSymplectic Elements at Oxford2005Rittscher, JTu, PKrahnstoever, NThe main focus of this work is the integration of feature grouping and model based segmentation into one consistent framework. The algorithm is based on partitioning a given set of image features using a likelihood function that is parameterized on the shape and location of potential individuals in the scene, using a variant of the EM formulation, maximum likelihood estimates of both the model parameters and the grouping are obtained simultaneously. The resulting algorithm performs global optimization and generates accurate results even when decisions can not be made using local context alone. An important feature of the algorithm is that the number of people in the scene is not modeled explicitly. As a result no prior knowledge or assumed distributions are required. The approach is shown to be robust with respect to partial occlusion, shadows, clutter, and can operate over a large range of challenging view angles including those that are parallel to the ground plane. Comparisons with existing crowd segmentation systems are made and the utility of coupling crowd segmentation with a temporal tracking system is demonstrated. © 2005 IEEE. |
| spellingShingle | Rittscher, J Tu, P Krahnstoever, N Simultaneous estimation of segmentation and shape |
| title | Simultaneous estimation of segmentation and shape |
| title_full | Simultaneous estimation of segmentation and shape |
| title_fullStr | Simultaneous estimation of segmentation and shape |
| title_full_unstemmed | Simultaneous estimation of segmentation and shape |
| title_short | Simultaneous estimation of segmentation and shape |
| title_sort | simultaneous estimation of segmentation and shape |
| work_keys_str_mv | AT rittscherj simultaneousestimationofsegmentationandshape AT tup simultaneousestimationofsegmentationandshape AT krahnstoevern simultaneousestimationofsegmentationandshape |