A survey of human motion analysis using depth imagery

By Lulu Chen, Hong Wei, James M. Ferryman. 2013

399 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 3D Vision & Pose

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Citations in the corpus

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis (2016, background): “For a more extensive list of current 3D activity analysis datasets and methods, readers are referred to these survey papers [47, 1, 5, 12, 21, 45, 3].”
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “Readers are referred to these survey papers [31], [32], [33], [34], [35], [36] for a more extensive list of the current 3D activity analysis datasets and methods.”
RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “This device equipped with intelligent algorithms is contributing to various applications, such as 3D-simultaneous localization and mapping (SLAM) [39, 54], people tracking [69], object recognition [11] and human activity analysis [13, 57], etc.”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Several earlier surveys were also published to review methods to recognize human poses, motions, gestures, and activities [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], as well as their applications [49], [50].”
RGB-D-based Human Motion Recognition with Deep Learning: A Survey (2017, background): “Several survey papers have summarized the research on human motion recognition using RGB-D data (Aggarwal and Xia, 2014; Chen et al., 2013; Cheng et al., 2016; Escalera et al., 2016; Han et al., 2017; Presti and La Cascia, 2016; Ye et al., 2013; Zhang et al., 2016b). Specifically, Chen et al. (2013)”
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding (2017, background): “For a more extensive conclusion of activity analysis we refer to corresponding survey papers [5, 56, 1, 7].”
Survey on 3D Hand Gesture Recognition (2016, background): “Various 3D depth sensing technologies have been well reviewed by [15], [16], and [29], which mainly introduce the working mechanisms of different depth sensors.”
3D skeleton-based human action classification: A survey (2016, result): “In contrast to very recent surveys such as [66,67] which focus on the use of depths cameras in several application domains or present a review of the literature on activity recognition in depth maps, in this survey we focus mostly on action classi ﬁ cation from skeletal data.”
Monocular human pose estimation: A survey of deep learning-based methods (2020, background): “More recent surveys were mainly focusing on relatively narrow directions, such as RGB-D-based action recognition(Chen et al., 2013; Wang et al., 2018b), 3D HPE (Sminchisescu, 2008; Holte et al., 2012; Saraﬁanos et al., 2016), model-based HPE (Holte et al., 2012; Perez-Sala et al., 2014), body…”
3D human action segmentation and recognition using pose kinetic energy (2014, background): “[7] summarize research on pose estimation and action recognition using depth images.”
A survey of depth and inertial sensor fusion for human action recognition (2015, methodology): “…in [24], wearable inertial sensors were employed to recognize daily activities by using artificial neural networks within a tree Chen et al. [19] 2013 A survey of human motion analysis using depth imagery A review addressing articulated 3D body modelling for human pose estimation and human…”
Fusing Spatiotemporal Features and Joints for 3D Action Recognition (2013, methodology): “Thus, using the Kinect sensor, three channels (RGB, depth and skeleton joint positions) of information can be provided, which can benefit robotics and human centered computing problems, and bring a broader scope for action recognition research [4].”
Fusing Multiple Features for Depth-Based Action Recognition (2015, methodology): “Therefore, using the Kinect sensor, three channels (RGB, depth, and skeleton joint positions) of data are provided, which not only bring great benefits for robotics and human-centered computing but also give a broader scope for action recognition as well [Chen et al. 2013].”
Human Action Recognition From Various Data Modalities: A Review (2020, background): “Single modality-based HAR has been extensively investigated in the past decades [6], [17], [18], [19], [20], [21], [22].”
Skeleton-based Human Activity Recognition for Video Surveillance (2015, background): “The difficulties of action recognition come from several aspects [3, 4].”