Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU
The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to re...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/528080 |
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| _version_ | 1832565705077161984 |
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| author | Jinwei Wang Xirong Ma Yuanping Zhu Jizhou Sun |
| author_facet | Jinwei Wang Xirong Ma Yuanping Zhu Jizhou Sun |
| author_sort | Jinwei Wang |
| collection | DOAJ |
| description | The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures. |
| format | Article |
| id | doaj-art-379c8c6e21c847ca93a729e0a553f6ad |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-379c8c6e21c847ca93a729e0a553f6ad2025-02-03T01:06:51ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/528080528080Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPUJinwei Wang0Xirong Ma1Yuanping Zhu2Jizhou Sun3School of Computer Science and Technology, Tianjin University, Tianjin 300072, ChinaCollege of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, ChinaCollege of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, ChinaSchool of Computer Science and Technology, Tianjin University, Tianjin 300072, ChinaThe active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.http://dx.doi.org/10.1155/2014/528080 |
| spellingShingle | Jinwei Wang Xirong Ma Yuanping Zhu Jizhou Sun Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU The Scientific World Journal |
| title | Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU |
| title_full | Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU |
| title_fullStr | Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU |
| title_full_unstemmed | Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU |
| title_short | Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU |
| title_sort | efficient parallel implementation of active appearance model fitting algorithm on gpu |
| url | http://dx.doi.org/10.1155/2014/528080 |
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