Human gesture recognition using Kinect camera

Patsadu, Orasa; Nukoolkit, Chakarida; Watanapa, Bunthit

doi:10.1109/jcsse.2012.6261920

Cited by 175 publications

(95 citation statements)

References 6 publications

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“…However, the authors only recognized three postures and did not provide the recognition results of test subjects facing in different directions. The proposed method not only recognizes five postures (including the three postures recognized in [27]), it also deals with test subjects facing in different directions. Note that the recognition methods in [25][26][27] were based on skeletal joint positions of the subject drawn from the Kinect software development kit (SDK).…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Human Posture Recognition Based on Images Captured by the Kinect Sensor

Wang

Chang

Haung

et al. 2016

International Journal of Advanced Robotic Systems

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In this paper we combine several image processing techniques with the depth images captured by a Kinect sensor to successfully recognize the five distinct human postures of sitting, standing, stooping, kneeling, and lying.The proposed recognition procedure first uses background subtraction on the depth image to extract a silhouette contour of a human. Then, a horizontal projection of the silhouette contour is employed to ascertain whether or not the human is kneeling. If the figure is not kneeling, the star skeleton technique is applied to the silhouette contour to obtain its feature points. We can then use the feature points together with the centre of gravity to calculate the feature vectors and depth values of the body. Next, we input the feature vectors and the depth values into a pre-trained LVQ (learning vector quantization) neural network; the outputs of this will determine the postures of sitting (or standing), stooping, and lying. Lastly, if an output indicates sitting or standing, one further, similar feature identification technique is needed to confirm this output. Based on the results of many experiments, using the proposed method, the rate of successful recognition is higher than 97% in the test data, even though the subjects of the experiments may not have been facing the Kinect sensor and may have had different statures. The proposed method can be called a "hybrid recognition method", as many techniques are combined in order to achieve a very high recognition rate paired with a very short processing time.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

Human Posture Recognition Based on Images Captured by the Kinect Sensor

Wang

Chang

Haung

et al. 2016

International Journal of Advanced Robotic Systems

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“…4. Specifically, given K human joints with [175] Vector of Joints Conc Lowlv Hand Patsadu et al [176] Vector of Joints Conc Lowlv Hand Huang and Kitani [177] Cost Topology Stat Lowlv Hand Devanne et al [178] Motion Units Conc Manif Hand Wang et al [179] Motion Poselets BoW Body Dict Wei et al [180] Structural Prediction Conc Lowlv Hand Gupta et al [181] 3D Pose w/o Body Parts Conc Lowlv Hand Amor et al [182] Skeleton's Shape Conc Manif Hand Sheikh et al [183] Action Space Conc Lowlv Hand Yilma and Shah [184] Multiview Geometry Conc Lowlv Hand Gong et al [185] Structured Time Conc Manif Hand Rahmani and Mian [186] Knowledge Transfer BoW Lowlv Dict Munsell et al [187] Motion Biometrics Stat Lowlv Hand Lillo et al [188] Composable Activities BoW Lowlv Dict Wu et al [189] Watch-n-Patch BoW Lowlv Dict Gong and Medioni [190] Dynamic Manifolds BoW Manif Dict Han et al [191] Hierarchical Manifolds BoW Manif Dict Slama et al [192,193] Grassmann Manifolds BoW Manif Dict Devanne et al [194] Riemannian Manifolds Conc Manif Hand Huang et al [195] Shape Tracking Conc Lowlv Hand Devanne et al [196] Riemannian Manifolds Conc Manif Hand Zhu et al [197] RNN with LSTM Conc Lowlv Deep Chen et al [198] EnwMi Learning BoW Lowlv Dict Hussein et al [199] Covariance of 3D Joints Stat Lowlv Hand Shahroudy et al [200] MMMP BoW Body Unsup Jung and Hong [201] Elementary Moving Pose BoW Lowlv Dict Evangelidis et al [202] Skeletal Quad Conc Lowlv Hand Azary and Savakis [203] Grassmann Manifolds Conc Manif Hand Barnachon et al [204] Hist. of Action Poses Stat Lowlv Hand Shahroudy et al [205] Feature Fusion BoW Body Unsup Cavazza et al [206] Kernelized-COV Stat Lowlv Hand …”

Section: Representations Based On Raw Joint Positionsmentioning

confidence: 99%

Space-time representation of people based on 3D skeletal data: A review

Han

Reily

Hoff

et al. 2017

Computer Vision and Image Understanding

264

188

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Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Representations can be broadly categorized into two groups, depending on whether they use RGB-D information or 3D skeleton data. Recently, skeletonbased human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of benchmark datasets with skeleton data, and discuss potential future research directions.

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“…Regarding the set of postures identified postures, it is possible to recognize them using depth cameras such as the Microsoft Kinect sensor (Le, Nguyen & Nguyen, 2013;Xiao, Mengyin, Yi, & Ningyi, 2012). This type of cameras have also been used to recognize predefined gestures (Li, 2012;Patsadu, Nukoolkit, & Watanapa, 2012;Ren, Yuan, Meng, & Zhang, 2013).…”

Section: Technical Nonverbal Communication Behaviorsmentioning

confidence: 99%

Presentation Trainer: what experts and computers can tell about your nonverbal communication

Schneider

Börner

Rosmalen

et al. 2017

Computer Assisted Learning

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The ability to present effectively is essential for professionals; therefore oral communication courses have become part of the curricula for higher education studies. However, speaking in public is still a challenge for many graduates. To tackle this problem, driven by the recent advances in computer vision techniques and prosody analysis, multimodal tools have been designed to support the development of public speaking skills. One of these tools is the Presentation Trainer, a research prototype able to provide learners with real-time feedback on a set of nonverbal communication aspects. Despite initial positive evaluations, the application still lacks grounding in a valid assessment model for nonverbal communication aspects in the context of presentations. To come up with such a model, we conducted semi-structured interviews with experts in the public speaking domain. Furthermore, the objective of these interviews was also to have a formative evaluation of the Presentation Trainer, analyzing how it suits with common practices for teaching and learning public speaking skills. The results of this study identify 131 nonverbal communication practices that affect the quality of a presentation, and summarize experts' points of view regarding multimodal public speaker instructors.

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Human gesture recognition using Kinect camera

Cited by 175 publications

References 6 publications

Human Posture Recognition Based on Images Captured by the Kinect Sensor

Human Posture Recognition Based on Images Captured by the Kinect Sensor

Space-time representation of people based on 3D skeletal data: A review

Presentation Trainer: what experts and computers can tell about your nonverbal communication

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