Free-Viewpoint Video of Human Actors Using Multiple Handheld Kinects

Ye, Genzhi; Liu, Yebin; Deng, Yue; Hasler, Nils; Ji, Xiangyang; Dai, Qionghai; Theobalt, Christian

doi:10.1109/tcyb.2013.2272321

Cited by 27 publications

(5 citation statements)

References 48 publications

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“…Positioning systems, such as OpenPTrack [ 3 ], which creates a scalable, multi-camera solution, have been developed for tracking people. In addition, Ye et al [ 14 ] developed a method to detect and identify several people that are occluded by others in a scene. Furthermore, a third approach to obtain the position of users, the method Kinect Positioning System (KPS), is analyzed in [ 5 ].…”

Section: Overview Of Related Workmentioning

confidence: 99%

An Improved Indoor Positioning System Using RGB-D Cameras and Wireless Networks for Use in Complex Environments

Domingo

Cerrada

Valero

et al. 2017

Sensors

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This work presents an Indoor Positioning System to estimate the location of people navigating in complex indoor environments. The developed technique combines WiFi Positioning Systems and depth maps, delivering promising results in complex inhabited environments, consisting of various connected rooms, where people are freely moving. This is a non-intrusive system in which personal information about subjects is not needed and, although RGB-D cameras are installed in the sensing area, users are only required to carry their smart-phones. In this article, the methods developed to combine the above-mentioned technologies and the experiments performed to test the system are detailed. The obtained results show a significant improvement in terms of accuracy and performance with respect to previous WiFi-based solutions as well as an extension in the range of operation.

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Section: Overview Of Related Workmentioning

confidence: 99%

An Improved Indoor Positioning System Using RGB-D Cameras and Wireless Networks for Use in Complex Environments

Domingo

Cerrada

Valero

et al. 2017

Sensors

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“…Our system is not confined to a fixed camera setup, but can work effectively for a hand-held acquisition, if required. We use a software-based synchronization similar to Ye et al (2013) for the multi-view acquisition. We use the Kinect SDK to acquire RGB, depth and skeleton data.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…In contrast, Berger et al (2011) employed four Kinects for unsynchronized marker-less motion capture. Ye et al (2013) employed three hand-held Kinects for marker-less performance capture. Caputo et al (2012) employed multiple Kinects for hand gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Multi-View RGB-D Video Analysis and Fusion for 360 Degrees Unified Motion Reconstruction

Ahmed

2017

Journal of Computer Science

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We present a new method for capturing human motion over 360 degrees by the fusion of multi-view RGB-D video data from Kinect sensors. Our method is able to reconstruct the unified human motion from fused RGB-D and skeletal data over 360 degrees and create a unified skeletal animation. We make use of all three streams: RGB, depth and skeleton, along with the joint tracking confidence state from Microsoft Kinect SDK to find the correctly oriented skeletons and merge them together to get a uniform measurement of human motion resulting in a unified skeletal animation. We quantitatively validate the goodness of the unified motion using two evaluation techniques. Our method is easy to implement and provides a simple solution of measuring and reconstructing a 360 degree plausible unified human motion that would not be possible to capture with a single Kinect due to tracking failures, self-occlusions, limited field of view and subject orientation.

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“…With depth sensors, the actor's pose can be more easily estimated using, for example, skeleton tracking [21]. For example, Ye et al [29] use three handheld Kinect sensors in order to capture an outdoor scene with multiple actors. The point clouds from each Kinect are integrated in order to reconstruct and texture a surface model of each actor, and the estimated human pose is used to help with this.…”

Section: Novel View Synthesismentioning

confidence: 99%

Increasing pose comprehension through augmented reality reenactment

Dayrit

Nakashima

Sato

et al. 2015

Multimed Tools Appl

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Standard video does not capture the 3D aspect of human motion, which is important for comprehension of motion that may be ambiguous. In this paper, we apply augmented reality (AR) techniques to give viewers insight into 3D motion by allowing them to manipulate the viewpoint of a motion sequence of a human actor using a handheld mobile device. The motion sequence is captured using a single RGB-D sensor, which is easier for a general user, but presents the unique challenge of synthesizing novel views using images captured from a single viewpoint. To address this challenge, our proposed system reconstructs a 3D model of the actor, then uses a combination of the actor's pose and viewpoint similarity to find appropriate images to texture it. The system then renders the 3D model on the mobile device using visual SLAM to create a map in order to use it to estimate the mobile device's camera pose relative to the original capturing environment. We call this novel view of a moving human actor a reenactment, and evaluate its usefulness and quality with an experiment and a survey.

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Free-Viewpoint Video of Human Actors Using Multiple Handheld Kinects

Cited by 27 publications

References 48 publications

An Improved Indoor Positioning System Using RGB-D Cameras and Wireless Networks for Use in Complex Environments

An Improved Indoor Positioning System Using RGB-D Cameras and Wireless Networks for Use in Complex Environments

Multi-View RGB-D Video Analysis and Fusion for 360 Degrees Unified Motion Reconstruction

Increasing pose comprehension through augmented reality reenactment

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