Outdoor human motion capture using inverse kinematics and von mises-fisher sampling

Pons-Moll, Gerard; Baak, Andreas; Gall, Jüergen; Leal-Taixé, Laura; Müller, Meinard; Seidel, Hans‐Peter; Rosenhahn, Bodo

doi:10.1109/iccv.2011.6126375

Cited by 76 publications

(59 citation statements)

References 26 publications

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“…Generative methods are susceptible to local minima, and thus require good initial pose estimates, regardless of the optimization scheme used. The pose is typically inferred using local optimization [274,275,276,277,278] or stochastic search [279,280,281]. …”

Section: Methodsologiesmentioning

confidence: 99%

Human Pose Estimation from Monocular Images: A Comprehensive Survey

Gong

Zhang

Gonzàlez

et al. 2016

Sensors

130

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Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.

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Section: Methodsologiesmentioning

confidence: 99%

Human Pose Estimation from Monocular Images: A Comprehensive Survey

Gong

Zhang

Gonzàlez

et al. 2016

Sensors

130

View full text Add to dashboard Cite

show abstract

“…Hence, 4D reconstruction with time being the fourth dimension is used for accurate capture of human motion or facial expressions while ensuring spatio-temporal coherence across frames. A common approach is to fit pre-defined shape templates, which encode the topology and sometimes also the coarse geometry of the captured shape, while solving for perframe pose variations de Aguiar et al 2008;Vlasic et al 2008;Bradley et al 2008;Pons-Moll et al 2011]. When successive scans have small relative deformation and share large overlapping regions, adequate feature correspondences can be found between consecutive frames.…”

Section: Related Workmentioning

confidence: 99%

Analyzing growing plants from 4D point cloud data

Li¹,

Fan²,

Mitra

et al. 2013

ACM Trans. Graph.

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Day 35 bifurcation bifurcation Decay Decay Decay Decay budding budding Decay Decay bifurcation bifurcation budding budding bifurcation bifurcationFigure 1: (Top) Dishlia growth time lapse point cloud over 5 weeks, with classified organs and detected budding, bifurcation and decay events. (Bottom) The extracted events are then used to bring a static plant model to life with both motion and growth. AbstractStudying growth and development of plants is of central importance in botany. Current quantitative are either limited to tedious and sparse manual measurements, or coarse image-based 2D measurements. Availability of cheap and portable 3D acquisition devices has the potential to automate this process and easily provide scientists with volumes of accurate data, at a scale much beyond the realms of existing methods. However, during their development, plants grow new parts (e.g., vegetative buds) and bifurcate to different components -violating the central incompressibility assumption made by existing acquisition algorithms, which makes these algorithms unsuited for analyzing growth. We introduce a framework to study plant growth, particularly focusing on accurate localization and tracking topological events like budding and bifurcation. This is achieved by a novel forward-backward analysis, wherein we track robustly detected plant components back in time to ensure correct spatio-temporal event detection using a locally adapting threshold. We evaluate our approach on several groups of time lapse scans, often ranging from days to weeks, on a diverse set of plant species and use the results to animate static virtual plants or directly attach them to physical simulators.

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“…The ASTAR dataset [36] used a ShapeHand data-glove [23], but wearing the glove influences the captured hand images, and to some extent hinders free hand articulation. In the works of [17,32], full human body pose esimtation was treated as a state estimation problem given magnetic sensor and depth data. More recently, less intrusive magnetic sensors have been used for finger tip annotation in the HandNet dataset [34], which exploits a similar annotation setting as our benchmark with trakSTAR magnetic sensors [10].…”

Section: Existing Benchmarksmentioning

confidence: 99%

BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis

Yuan

Stenger

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

195

225

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In this paper we introduce a large-scale hand pose dataset, collected using a novel capture method. Existing datasets are either generated synthetically or captured using depth sensors: synthetic datasets exhibit a certain level of appearance difference from real depth images, and real datasets are limited in quantity and coverage, mainly due to the difficulty to annotate them. We propose a tracking system with six 6D magnetic sensors and inverse kinematics to automatically obtain 21-joints hand pose annotations of depth maps captured with minimal restriction on the range of motion. The capture protocol aims to fully cover the natural hand pose space. As shown in embedding plots, the new dataset exhibits a significantly wider and denser range of hand poses compared to existing benchmarks. Current state-of-the-art methods are evaluated on the dataset, and we demonstrate significant improvements in cross-benchmark performance. We also show significant improvements in egocentric hand pose estimation with a CNN trained on the new dataset.

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Outdoor human motion capture using inverse kinematics and von mises-fisher sampling

Abstract: This is the supplemental material for [5]. It contains a more detailed description of the closed form algorithm to compute inverse kinematics based on the Paden-Kahan subproblems. For an extended and more detailed version of [5] we refer the reader to [7].

Cited by 76 publications

References 26 publications

Human Pose Estimation from Monocular Images: A Comprehensive Survey

Human Pose Estimation from Monocular Images: A Comprehensive Survey

Analyzing growing plants from 4D point cloud data

BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis

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