Literature DB >> 23807480

Enhanced computer vision with Microsoft Kinect sensor: a review.

Jungong Han, Ling Shao, Dong Xu, Jamie Shotton.   

Abstract

With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.

Entities:  

Mesh:

Year:  2013        PMID: 23807480     DOI: 10.1109/TCYB.2013.2265378

Source DB:  PubMed          Journal:  IEEE Trans Cybern        ISSN: 2168-2267            Impact factor:   11.448


  55 in total

1.  MarmoDetector: A novel 3D automated system for the quantitative assessment of marmoset behavior.

Authors:  Taiki Yabumoto; Fumiaki Yoshida; Hideaki Miyauchi; Kousuke Baba; Hiroshi Tsuda; Kensuke Ikenaka; Hideki Hayakawa; Nozomu Koyabu; Hiroki Hamanaka; Stella M Papa; Masayuki Hirata; Hideki Mochizuki
Journal:  J Neurosci Methods       Date:  2019-04-01       Impact factor: 2.390

2.  Fast, accurate, small-scale 3D scene capture using a low-cost depth sensor.

Authors:  Nicole Carey; Radhika Nagpal; Justin Werfel
Journal:  IEEE Winter Conf Appl Comput Vis       Date:  2017-05-15

3.  An automated behavior analysis system for freely moving rodents using depth image.

Authors:  Zheyuan Wang; S Abdollah Mirbozorgi; Maysam Ghovanloo
Journal:  Med Biol Eng Comput       Date:  2018-03-21       Impact factor: 2.602

4.  The Present and Future of Robotic Technology in Rehabilitation.

Authors:  Jeffrey Laut; Maurizio Porfiri; Preeti Raghavan
Journal:  Curr Phys Med Rehabil Rep       Date:  2016-11-19

5.  A Video-Based Classification System for Assessing Locomotor Skills in Children.

Authors:  Daniel H K Chow; Wilson H W Cheng; Simone S M Tam
Journal:  J Sports Sci Med       Date:  2020-08-13       Impact factor: 2.988

6.  A Cognitive Sample Consensus Method for the Stitching of Drone-Based Aerial Images Supported by a Generative Adversarial Network for False Positive Reduction.

Authors:  Jeong-Kweon Seo
Journal:  Sensors (Basel)       Date:  2022-03-23       Impact factor: 3.576

7.  Kinect as a tool for gait analysis: validation of a real-time joint extraction algorithm working in side view.

Authors:  Enea Cippitelli; Samuele Gasparrini; Susanna Spinsante; Ennio Gambi
Journal:  Sensors (Basel)       Date:  2015-01-14       Impact factor: 3.576

8.  A Kinect-based real-time compressive tracking prototype system for amphibious spherical robots.

Authors:  Shaowu Pan; Liwei Shi; Shuxiang Guo
Journal:  Sensors (Basel)       Date:  2015-04-08       Impact factor: 3.576

9.  RGB-D SLAM Combining Visual Odometry and Extended Information Filter.

Authors:  Heng Zhang; Yanli Liu; Jindong Tan; Naixue Xiong
Journal:  Sensors (Basel)       Date:  2015-07-30       Impact factor: 3.576

10.  Self-organizing neural integration of pose-motion features for human action recognition.

Authors:  German I Parisi; Cornelius Weber; Stefan Wermter
Journal:  Front Neurorobot       Date:  2015-06-09       Impact factor: 2.650

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