A data-driven approach for real-time full body pose reconstruction from a depth camera

Baak, Andreas; Müller, Meinard; Bharaj, Gaurav; Seidel, Hans‐Peter; Theobalt, Christian

doi:10.1109/iccv.2011.6126356

Cited by 236 publications

(186 citation statements)

References 23 publications

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“…Thus, we exploit the approach proposed in [13] to describe the topology of point clouds. Then, a new strategy proposed in [2] for searching the end-effectors from its topology representation is integrated into the proposed system, providing a better performance than the strategy used in [13].…”

Section: Single Frame End-effector Estimationmentioning

confidence: 99%

“…We exploit the strategy used in [2] to detect end-effectors on the graph. According to this strategy, we first search the node with the longest shortest path to the target node which is set to be the graph root initially.…”

Section: -Edges and Weightsmentioning

confidence: 99%

“…[11] propose to use optical flow to handle the topology change problem. Similar to [2], a graph is constructed to represent the topology of the point cloud. Then motion information is employed to disambiguate body parts when occlusion and body part attachment (that is, when end effectors are connected to other body parts) occur.…”

Section: Related Workmentioning

confidence: 99%

“…Baak el al. [2] construct a weighted graph for point cloud data by taking each point on the point cloud as a graph node and exploiting the neighborhood structures in the pixel domain to build edges between them. Then, edges are weighted as the Euclidean distances between connected node pairs.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Time Consistent Estimation of End-Effectors from RGB-D Data

Lin

Casas

Pardàs

2016

Image and Video Technology

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Abstract. End-effectors are usually related to the location of the free end of a kinematic chain. Each of them contains rich structure information about the entity. Hence, estimating stable end-effectors of different entities enables robust tracking as well as a generic representation. In this paper, we present a system for end-effector estimation from RGB-D stream data. Instead of relying on a specific pose or configuration for initialization, we exploit time coherence without making any assumption with respect to the prior knowledge. This makes the estimation process more robust in a predict-update framework. Qualitative and quantitative experiments are performed against the reference method with promising results.

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Section: Single Frame End-effector Estimationmentioning

confidence: 99%

Section: -Edges and Weightsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Time Consistent Estimation of End-Effectors from RGB-D Data

Lin

Casas

Pardàs

2016

Image and Video Technology

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“…Body pose is then inferred from the body parts' centroids. Baak et al [20] combine local feature matching with a database lookup, achieving a fast and robust end-effector tracking. Zhu et al [21] propose a tracking algorithm which exploits temporal consistency to estimate the pose of a constrained human model.…”

Section: Related Workmentioning

confidence: 99%

Detecting end-effectors on 2.5D data using geometric deformable models: Application to human pose estimation

Suau

Ruiz-Hidalgo

Casas

2013

Computer Vision and Image Understanding

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End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body.

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Real‐time motion data annotation via action string

Xiao

Zhuang

Zhang

et al. 2014

Computer Animation & Virtual

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Even though there is an explosive growth of motion capture data, there is still a lack of efficient and reliable methods to automatically annotate all the motions in a database. Moreover, because of the popularity of mocap devices in home entertainment systems, real-time human motion annotation or recognition becomes more and more imperative. This paper presents a new motion annotation method that achieves both the aforementioned two targets at the same time. It uses a probabilistic pose feature based on the Gaussian Mixture Model to represent each pose. After training a clustered pose feature model, a motion clip could be represented as an action string. Then, a dynamic programming-based string matching method is introduced to compare the differences between action strings. Finally, in order to achieve the real-time target, we construct a hierarchical action string structure to quickly label each given action string. The experimental results demonstrate the efficacy and efficiency of our method.

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A data-driven approach for real-time full body pose reconstruction from a depth camera

Abstract: Abstract

Cited by 236 publications

References 23 publications

Time Consistent Estimation of End-Effectors from RGB-D Data

Time Consistent Estimation of End-Effectors from RGB-D Data

Detecting end-effectors on 2.5D data using geometric deformable models: Application to human pose estimation

Real‐time motion data annotation via action string

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