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Forest Walk Methods for Localizing Body Joints from Single Depth Image.

Jung HY, Lee S, Heo YS, Yun ID - PLoS ONE (2015)

Bottom Line: A regression forest is trained to estimate the probability distribution to the direction or offset toward the particular joint, relative to the adjacent position.The distribution for next position is found from traversing the regression tree from new position.The continual position sampling through 3D space will eventually produce an expectation of sample positions, which we estimate as the joint position.

View Article: PubMed Central - PubMed

Affiliation: Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea.

ABSTRACT
We present multiple random forest methods for human pose estimation from single depth images that can operate in very high frame rate. We introduce four algorithms: random forest walk, greedy forest walk, random forest jumps, and greedy forest jumps. The proposed approaches can accurately infer the 3D positions of body joints without additional information such as temporal prior. A regression forest is trained to estimate the probability distribution to the direction or offset toward the particular joint, relative to the adjacent position. During pose estimation, the new position is chosen from a set of representative directions or offsets. The distribution for next position is found from traversing the regression tree from new position. The continual position sampling through 3D space will eventually produce an expectation of sample positions, which we estimate as the joint position. The experiments show that the accuracy is higher than current state-of-the-art pose estimation methods with additional advantage in computation time.

No MeSH data available.


Related in: MedlinePlus

Example results of the RTW from EVAL test data.64 RTW steps are taken for each joint. The RTW paths are drawn at the top row, the expectations of RTW steps are used to find joint positions in bottom row. The pose estimation from a single image takes less than 1 millisecond.
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pone.0138328.g007: Example results of the RTW from EVAL test data.64 RTW steps are taken for each joint. The RTW paths are drawn at the top row, the expectations of RTW steps are used to find joint positions in bottom row. The pose estimation from a single image takes less than 1 millisecond.

Mentions: Finally, few qualitative examples of our method from EVAL test sequences are shown in Fig 7. In the figure, 64 forest walk steps are taken with 5 cm step size. Both the forest walk paths and pose expectations are shown. Note that very hard poses like hand-stand, crouching, and cross-punch are efficiently and accurately estimated with our approach.


Forest Walk Methods for Localizing Body Joints from Single Depth Image.

Jung HY, Lee S, Heo YS, Yun ID - PLoS ONE (2015)

Example results of the RTW from EVAL test data.64 RTW steps are taken for each joint. The RTW paths are drawn at the top row, the expectations of RTW steps are used to find joint positions in bottom row. The pose estimation from a single image takes less than 1 millisecond.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581738&req=5

pone.0138328.g007: Example results of the RTW from EVAL test data.64 RTW steps are taken for each joint. The RTW paths are drawn at the top row, the expectations of RTW steps are used to find joint positions in bottom row. The pose estimation from a single image takes less than 1 millisecond.
Mentions: Finally, few qualitative examples of our method from EVAL test sequences are shown in Fig 7. In the figure, 64 forest walk steps are taken with 5 cm step size. Both the forest walk paths and pose expectations are shown. Note that very hard poses like hand-stand, crouching, and cross-punch are efficiently and accurately estimated with our approach.

Bottom Line: A regression forest is trained to estimate the probability distribution to the direction or offset toward the particular joint, relative to the adjacent position.The distribution for next position is found from traversing the regression tree from new position.The continual position sampling through 3D space will eventually produce an expectation of sample positions, which we estimate as the joint position.

View Article: PubMed Central - PubMed

Affiliation: Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea.

ABSTRACT
We present multiple random forest methods for human pose estimation from single depth images that can operate in very high frame rate. We introduce four algorithms: random forest walk, greedy forest walk, random forest jumps, and greedy forest jumps. The proposed approaches can accurately infer the 3D positions of body joints without additional information such as temporal prior. A regression forest is trained to estimate the probability distribution to the direction or offset toward the particular joint, relative to the adjacent position. During pose estimation, the new position is chosen from a set of representative directions or offsets. The distribution for next position is found from traversing the regression tree from new position. The continual position sampling through 3D space will eventually produce an expectation of sample positions, which we estimate as the joint position. The experiments show that the accuracy is higher than current state-of-the-art pose estimation methods with additional advantage in computation time.

No MeSH data available.


Related in: MedlinePlus