Markerless tracking of complex human motions from multiple views

Kehl, Roland; Gool, Luc Van

doi:10.1016/j.cviu.2006.07.010

Cited by 90 publications

(48 citation statements)

References 20 publications

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“…Several systems have been presented [8,10], yet most of them are computationally demanding and lack a proof of robustness necessary for long-term tracking. In recent years, a tendency towards learning based methods has been observed, to overcome the computational burden of searching the high-dimensional human pose space.…”

Section: Related Workmentioning

confidence: 99%

The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition

Tenorth

Bandouch

Beetz

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

167

162

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We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, fullbody motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations.

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Section: Related Workmentioning

confidence: 99%

The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition

Tenorth

Bandouch

Beetz

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

167

162

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“…We only focus our review on marker-less approaches. Camera-based methods have a long history [4], [5], [6], [7], [8]. However, monocular setups are highly sensitive to occlusions, while multi-view methods are in general computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Real-time human motion tracking using multiple depth cameras

Zhang

Sturm

Cremers

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we consider the problem of tracking human motion with a 22-DOF kinematic model from depth images. In contrast to existing approaches, our system naturally scales to multiple sensors. The motivation behind our approach, termed Multiple Depth Camera Approach (MDCA), is that by using several cameras, we can significantly improve the tracking quality and reduce ambiguities as for example caused by occlusions. By fusing the depth images of all available cameras into one joint point cloud, we can seamlessly incorporate the available information from multiple sensors into the pose estimation. To track the high-dimensional human pose, we employ state-of-the-art annealed particle filtering and partition sampling. We compute the particle likelihood based on the truncated signed distance of each observed point to a parameterized human shape model. We apply a coarse-tofine scheme to recognize a wide range of poses to initialize the tracker. In our experiments, we demonstrate that our approach can accurately track human motion in real-time (15Hz) on a GPGPU. In direct comparison to two existing trackers (OpenNI, Microsoft Kinect SDK), we found that our approach is significantly more robust for unconstrained motions and under (partial) occlusions.

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“…Multi-camera systems for 3D pose initialization were so far applied in controlled indoor environments. The near-perfect foreground segmentation resulting from the stationary background, together with the many cameras used (> 5), allows to recover pose by Shapefrom-Silhouette techniques (Cheung et al 2005a(Cheung et al , 2005bCorazza et al 2010;Kehl and Gool 2006;Mikic et al 2003;Starck and Hilton 2003;Sundaresan and Chellappa 2009). A new line of research goes beyond the recovery of pose parameters to the estimation of the non-rigid surface of the 3D human model (Balan et al 2007;Gall et al 2009).…”

Section: Previous Workmentioning

confidence: 99%

Multi-view 3D Human Pose Estimation in Complex Environment

Hofmann

Gavrila

2011

Int J Comput Vis

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We introduce a framework for unconstrained 3D human upper body pose estimation from multiple camera views in complex environment. Its main novelty lies in the integration of three components: single-frame pose recovery, temporal integration and model texture adaptation. Single-frame pose recovery consists of a hypothesis generation stage, in which candidate 3D poses are generated, based on probabilistic hierarchical shape matching in each camera view. In the subsequent hypothesis verification stage, the candidate 3D poses are re-projected into the other camera views and ranked according to a multi-view likelihood measure. Temporal integration consists of computing K-best trajectories combining a motion model and observations in a Viterbi-style maximum-likelihood approach. Poses that lie on the best trajectories are used to generate and adapt a texture model, which in turn enriches the shape likelihood measure used for pose recovery. The multiple trajectory hypotheses are used to generate pose predictions, augmenting the 3D pose candidates generated at the next time step.We demonstrate that our approach outperforms the stateof-the-art in experiments with large and challenging realworld data from an outdoor setting.

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Markerless tracking of complex human motions from multiple views

Cited by 90 publications

References 20 publications

The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition

The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition

Real-time human motion tracking using multiple depth cameras

Multi-view 3D Human Pose Estimation in Complex Environment

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