FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4288045203> ?p ?o ?g. }

• W4288045203 endingPage "185" @default.
• W4288045203 startingPage "172" @default.
• W4288045203 abstract "2D poses are insufficient to estimate human activities under the geometric ambiguities, occlusion, diverse appearance, and viewpoints inside the image. As a result, 3D posing has emerged as an attractive research area in recent decades due to its ability to distinguish human activities effectively. Human skeletal 3D posture is the foundation for 3D avatar creation, the construction of human 3D meshes, recognition of human actions or activities, augmented reality, etc. Therefore, estimating the correct human 3D skeleton or pose with minimum errors is an essential task. This research paper proposes a depth predictor module; an innovative and creative approach of depth prediction for evaluating the human 3D pose from a single 2D image. However, no predicted 3D pose is entirely free from errors. Therefore, it needs to reduce the inaccuracy after predicting the 3D pose. For this purpose, an enhancement approach, pose alignment , is used to reduce the positioning inaccuracies of the anticipated 3D pose. The specialty of the proposed model is that it does not require any training dataset, and it effectively estimates 3D poses under any scale variations. This proposed method is evaluated on different datasets: Human3.6M, HumanEva-I, NTU RGB + D, MPII, UAV-human and the Articulated Free Fall dataset. In terms of errors, the proposed approach achieves the minimum 3D pose error compared to the existing 3D pose estimation techniques. The proposed depth prediction approach reduces the average MPJPE value to 48.30, and the pose alignment module further reduces it to 40.61. In addition, the proposed model takes an inference time of 1.806 s per frame for a single person. • We introduced a novel concept for human 3D pose estimation from a single RGB image. • The specialty of this framework is that there is no need for any training dataset. • This is an algorithm-based system to estimate the 3D pose of the human body. • We introduced a Depth Predictor module, our novel concept to predict depth. • Proposed a pose alignment technique to enhance the accuracy of the predicted 3D pose. • This approach is novel and very interesting in the field of computer vision." @default.
• W4288045203 created "2022-07-27" @default.
• W4288045203 creator A5032170911 @default.
• W4288045203 creator A5054880944 @default.
• W4288045203 creator A5080447192 @default.
• W4288045203 date "2022-10-01" @default.
• W4288045203 modified "2023-10-01" @default.
• W4288045203 title "Enhancement of human 3D pose estimation using a novel concept of depth prediction with pose alignment from a single 2D image" @default.
• W4288045203 cites W2080985502 @default.
• W4288045203 cites W2099333815 @default.
• W4288045203 cites W2101032778 @default.
• W4288045203 cites W2170601200 @default.
• W4288045203 cites W2806070179 @default.
• W4288045203 cites W2962730651 @default.
• W4288045203 cites W2963207678 @default.
• W4288045203 cites W3037487778 @default.
• W4288045203 cites W3126541466 @default.
• W4288045203 cites W3128498158 @default.
• W4288045203 cites W3135657079 @default.
• W4288045203 doi "https://doi.org/10.1016/j.cag.2022.07.021" @default.
• W4288045203 hasPublicationYear "2022" @default.
• W4288045203 type Work @default.
• W4288045203 citedByCount "3" @default.
• W4288045203 countsByYear W42880452032022 @default.
• W4288045203 countsByYear W42880452032023 @default.
• W4288045203 crossrefType "journal-article" @default.
• W4288045203 hasAuthorship W4288045203A5032170911 @default.
• W4288045203 hasAuthorship W4288045203A5054880944 @default.
• W4288045203 hasAuthorship W4288045203A5080447192 @default.
• W4288045203 hasConcept C115961682 @default.
• W4288045203 hasConcept C153180895 @default.
• W4288045203 hasConcept C154945302 @default.
• W4288045203 hasConcept C31972630 @default.
• W4288045203 hasConcept C36613465 @default.
• W4288045203 hasConcept C41008148 @default.
• W4288045203 hasConcept C52102323 @default.
• W4288045203 hasConcept C84824328 @default.
• W4288045203 hasConceptScore W4288045203C115961682 @default.
• W4288045203 hasConceptScore W4288045203C153180895 @default.
• W4288045203 hasConceptScore W4288045203C154945302 @default.
• W4288045203 hasConceptScore W4288045203C31972630 @default.
• W4288045203 hasConceptScore W4288045203C36613465 @default.
• W4288045203 hasConceptScore W4288045203C41008148 @default.
• W4288045203 hasConceptScore W4288045203C52102323 @default.
• W4288045203 hasConceptScore W4288045203C84824328 @default.
• W4288045203 hasLocation W42880452031 @default.
• W4288045203 hasOpenAccess W4288045203 @default.
• W4288045203 hasPrimaryLocation W42880452031 @default.
• W4288045203 hasRelatedWork W1968716783 @default.
• W4288045203 hasRelatedWork W2004095265 @default.
• W4288045203 hasRelatedWork W2064877078 @default.
• W4288045203 hasRelatedWork W2076468506 @default.
• W4288045203 hasRelatedWork W2085100591 @default.
• W4288045203 hasRelatedWork W2128635338 @default.
• W4288045203 hasRelatedWork W2154613448 @default.
• W4288045203 hasRelatedWork W2286009621 @default.
• W4288045203 hasRelatedWork W2567319754 @default.
• W4288045203 hasRelatedWork W4306804934 @default.
• W4288045203 hasVolume "107" @default.
• W4288045203 isParatext "false" @default.
• W4288045203 isRetracted "false" @default.
• W4288045203 workType "article" @default.