Randomized trees for human pose detection

Rogez, Grégory; Rihan, Jonathan; Ramalingam, Srikumar; Orrite, Carlos; Torr, Philip H. S.

doi:10.1109/cvpr.2008.4587617

Cited by 139 publications

(86 citation statements)

References 20 publications

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“…In both cases, a holistic model has been proposed for classifying the body joints [15] or predicting their position [14] in the 3D space. In the image domain, Random Forests have been introduced for human body pose classification [11]. Finally, the combination of holistic and part-based methods has been explored by introducing the concept of Poselets [27] in the pictorial structures framework [2,28].…”

Section: Related Workmentioning

confidence: 99%

“…However, these methods can be sensitive to noisy input and cannot generalise to unknown poses. In order to cope with these problems, holistic approaches have relied on Random Forests [14,11,15]. In the depth domain, Random Forests have been used for classification [15] and regression [14].…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the holistic approaches predict directly the body skeleton by learning a mapping between image features and skeletons [9][10][11][12]. These approaches usually face problems with occlusion or noise because they require complete data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Holistic Human Pose Estimation with Regression Forests

Belagiannis

Amann

Navab

et al. 2014

Articulated Motion and Deformable Objects

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Abstract. In this work, we address the problem of human pose estimation in still images by proposing a holistic model for learning the appearance of the human body from image patches. These patches, which are randomly chosen, are used for extracting features and training a regression forest. During training, a mapping between image features and human poses, defined by joint offsets, is learned; while during prediction, the body joints are estimated with an efficient mode-seeking algorithm. In comparison to other holistic approaches, we can recover body poses from occlusion or noisy data. We demonstrate the power of our method in two publicly available datasets and propose a third one. Finally, we achieve state-of-the-art results in comparison to other approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Holistic Human Pose Estimation with Regression Forests

Belagiannis

Amann

Navab

et al. 2014

Articulated Motion and Deformable Objects

View full text Add to dashboard Cite

show abstract

“…Spatial-temporal shape templates can be created from motion capture data as suggested by Dimitrijevic et al 11 who then matched these templates against silhouettes in image sequences. Rogez et al 12 proposed randomized trees trained on histograms of oriented gradients (HOG) for human pose recognition from videos.…”

Section: Related Workmentioning

confidence: 99%

Automatic pose initialization of swimmers in videos

Ries¹,

Lienhart²

2010

SPIE Proceedings

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We propose an approach to the task of automatic pose initialization of swimmers in videos. Thus, our goal is to detect a swimmer inside a target video and assign an estimated position to her/his body parts. We first apply a non-skin-color filter to reduce the search space inside each target frame. We then match previously devised template sequences of Gaussian feature descriptors against sequences of feature vectors which are computed within the remaining image regions. Finally, relative average joint positions from annotated images featuring the key pose are assigned to the detection result and three-dimensional joint positions are estimated. We present detection results for test videos of three different swim strokes and examine the performance of four types of feature descriptors.

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“…2 (c), and the result of that test is used to determine which child node to choose in a decision tree. In a decision tree, the recursive node branching continues to the maximum depth or until no further information gain is possible [21]. We employed a depth-first manner, which recursively splits nodes until a maximum depth is reached.…”

Section: Scale Extension Based On Random Forestsmentioning

confidence: 99%

Image Categorization Using Scene-Context Scale Based on Random Forests

Kang

Nagahashi

Sugimoto

2011

IEICE Trans. Inf. & Syst.

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SUMMARYScene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective scale of local context to classify an image pixel in a scene. This paper presents random forests based image categorization using the scene-context scale. The proposed method uses random forests, which are ensembles of randomized decision trees. Since the random forests are extremely fast in both training and testing, it is possible to perform classification, clustering and regression in real time. We train multi-scale texton forests which efficiently provide both a hierarchical clustering into semantic textons and local classification in various scale levels. The scene-context scale can be estimated by the entropy of the leaf node in the multi-scale texton forests. For image categorization, we combine the classified category distributions in each scale and the estimated scene-context scale. We evaluate on the MSRC21 segmentation dataset and find that the use of the scene-context scale improves image categorization performance. Our results have outperformed the state-of-the-art in image categorization accuracy.

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Randomized trees for human pose detection

Cited by 139 publications

References 20 publications

Holistic Human Pose Estimation with Regression Forests

Holistic Human Pose Estimation with Regression Forests

Automatic pose initialization of swimmers in videos

Image Categorization Using Scene-Context Scale Based on Random Forests

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