Multi-task Forest for Human Pose Estimation in Depth Images

Lallemand, Joe; Pauly, Olivier; Schwarz, Loren; Tan, David Joseph; Ilić, Slobodan

doi:10.1109/3dv.2013.43

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Taylor et al [18] propose a learning regression forest for directly estimating the correspondences between input range images and body models, without the pixel classification step. Lallemand et al [11] integrate the activity information with pose regression, and formulate the problem as a joint regression-classification task which recovers the 3D body pose and classifies the performed activity. Buys et al [3] associate each pixel to a body component using randomized decision forests, and cluster all pixels into more robust component estimation.…”

Section: B Pixel Classification Approachesmentioning

confidence: 99%

“…Typically, Shotton et al [14], [15] integrate a simple but efficient feature: the depth difference of pairwise offset pixels, with the powerful randomized decision forests (RDF), and produce realtime and accurate pose recognition. Afterwards, several pixel classification approaches [11], [3] follow the idea and make various improvements. However, those approaches require large, varied offline training data for obtaining the decision forests, as well as many parameters (e.g., the number and depth of trees, the size of sampling window, and distribution of pairwise offsets within the window).…”

Section: Introductionmentioning

confidence: 99%

“…According to the methodology, research work on pose estimation via depth data can be roughly divided into four categories: the feature point approaches, the pixel classification approaches, the probabilistic graphical model approaches and others. In general, the feature point approaches [12], [1], [13], [8], [7] detect interest points of the range image ( with the extrema of geodesic distances of depth points), and then employ a database lookup scheme (e.g., [1]), classify each interesting point as a point of a body component using its local descriptors (e.g., [12]), or detect additional feature points as the secondary feature (e.g., [1], [13]) for discriminating human poses; the pixel classification approaches [14], [15], [18], [20], [11], [3], [9] determine which body component each pixel belongs to, using the so-called depth difference feature and random decision forests; the probabilistic graphical model approaches [26], [27], [6], [23] represent a human body as a set of rigid or non-rigid parts connected with pairwise constraints and formulate the localization of body parts as an optimization of the prior probability of part configurations. Different approaches fit for different applications of visual media, i.e., the codec and reconstruction of models of human body, the retrieval and analysis of human motion.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sparse Pose Regression via Componentwise Clustering Feature Point Representation

Liu

Kong

Wang

et al. 2016

IEEE Trans. Multimedia

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We propose 2D pose estimation from a single range image of human body, using sparse regression with a componentwise clustering feature point representation (CCFPR) model. CCFPR includes primary feature points and secondary feature points. The primary feature points consist of the torso center and five extremal points of human body, and further serve to classify all body pixels as the points of six body components. The secondary feature points are given by the cluster centers of each of the five components other than the torso, using Kmeans cluster. The human pose is obtained by learning a sparse projection matrix, which maps CCFPR to the skeleton points of human body, based on the assumption that each skeleton point be represented by a combination of a few feature points of associated body components. Experimental results on both virtual data and real data show that, under the sparse regression model with suitably selected cluster number, CCFPR outperforms the random decision forest approach and prediction results of KINECT SENSOR V2.

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Section: B Pixel Classification Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sparse Pose Regression via Componentwise Clustering Feature Point Representation

Liu

Kong

Wang

et al. 2016

IEEE Trans. Multimedia

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“…By contrast, we choose to apply our model in the operating room scenario which comprises significantly mutual occlusions between the target individuals and thus is much more challenging than the normal ones. In contrary to the concept of part-based models, the holistic models predict directly the body pose by learning a mapping between features and poses [1,6,23,25,29,44,47,59]. One very popular method to accomplish this task are random forests for human pose estimation from depth data [22,42].…”

Section: Related Workmentioning

confidence: 99%

Parsing human skeletons in an operating room

Belagiannis

Wang

Shitrit

et al. 2016

Machine Vision and Applications

Self Cite

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Multiple human pose estimation is an important yet challenging problem. In an Operating Room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localising and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures (3DPS) and 2D body part localization across all camera views, using Convolutional Neural Networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.

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“…Recently, RGB-D sensors were introduced for human motion capture using machine learning approaches such as random forests [17], [18]. Other model-based approaches [19], [20] produce impressive, accurate tracking and achieve real-time performance.…”

Section: Related Workmentioning

confidence: 99%

Pose estimation for a partially observable human body from RGB-D cameras

Diby

Charpillety

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Human pose estimation in realistic world conditions raises multiple challenges such as foreground extraction, background update and occlusion by scene objects. Most of existing approaches were demonstrated in controlled environments. In this paper, we propose a framework to improve the performance of existing tracking methods to cope with these problems. To this end, a robust and scalable framework is provided composed of three main stages. In the first one, a probabilistic occupancy grid updated with a Hidden Markov Model used to maintain an up-to-date background and to extract moving persons. The second stage uses component labelling to identify and track persons in the scene. The last stage uses an hierarchical particle filter to estimate the body pose for each moving person. Occlusions are handled by querying the occupancy grid to identify hidden body parts so that they can be discarded from the pose estimation process. We provide a parallel implementation that runs on CPU and GPU at 4 frames per second. We also validate the approach on our own dataset that consists of synchronized motion capture with a single RGB-D camera data of a person performing actions in challenging situations with severe occlusions generated by scene objects. We make this dataset available online.

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Multi-task Forest for Human Pose Estimation in Depth Images

Cited by 6 publications

References 20 publications

Sparse Pose Regression via Componentwise Clustering Feature Point Representation

Sparse Pose Regression via Componentwise Clustering Feature Point Representation

Parsing human skeletons in an operating room

Pose estimation for a partially observable human body from RGB-D cameras

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