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Author  |
Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla | ||||
| Title | Human Pose Estimation through A Novel Multi-View Scheme | Type | Conference Article | ||
| Year | 2022 | Publication | Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 | Abbreviated Journal | |
| Volume | 5 | Issue | Pages | 855-862 | |
| Keywords | Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach | ||||
| Abstract | This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human pose estimation problem. The proposed approach first obtains the human body joints of a set of images, which are captured from different views at the same time. Then, it enhances the obtained joints by using a multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements in the accuracy of body joints estimations. |
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| Notes | Approved | yes | |||
| Call Number | cidis @ cidis @ | Serial | 169 | ||
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Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca. | ||||
| Title | Human Body Pose Estimation in Multi-view Environments. | Type | Book Chapter | ||
| Year | 2022 | Publication | ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series | Abbreviated Journal | BOOK |
| Volume | 224 | Issue | Pages | 79-99 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 197 | ||
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| Author |
Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca | ||||
| Title | Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem | Type | Conference Article | ||
| Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
| Volume | 4 | Issue | Pages | 498-505 | |
| Keywords | Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. | ||||
| Abstract | This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consist of first training the network using pairs of images from the virtual-world scenario considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on the training due to the reduced number of pairs of real-images on most of the public data sets. |
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| ISSN | ISBN | 978-989758402-2 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 120 | ||
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| Author |
Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa | ||||
| Title | Deep learning based camera pose estimation in multi-view environment. | Type | Conference Article | ||
| Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
| Volume | Issue | Pages | 224-228 | ||
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| Abstract | This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 93 | ||
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