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Author  |
Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang | ||||
| Title | An Effective Solution to Double Counting Problem in Human Pose Estimation | Type | Miscellaneous | ||
| Year | 2015 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Pose estimation; double counting problem; mix-ture of parts Model | ||||
| Abstract | The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model or as latent variables for pedestrian detection. Even in the era of massive applications of deep learning techniques, the mixture of parts model is still effective in solving certain problems, especially in the case with limited numbers of training samples. In this paper, we consider using the mixture of parts model for pose estimation, wherein a tree structure is utilized for representing relations between connected body parts. This strategy facilitates training and inferencing of the model but suffers from double counting problems, where one detected body part is counted twice due to lack of constrains among unconnected body parts. To solve this problem, we propose a generalized solution in which various part attributes are captured by multiple features so as to avoid the double counted problem. Qualitative and quantitative experimental results on a public available dataset demonstrate the effectiveness of our proposed method. An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015]. |
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| Notes | ISE; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ GHG2015 | Serial | 2590 | ||
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