Image representation of pose-transition feature for 3D skeleton-based action recognition

Huynh‐The, Thien; Hua, Cam-Hao; Ngo, Trung-Thanh; Kim, Dong-Seong

doi:10.1016/j.ins.2019.10.047

Cited by 79 publications

(33 citation statements)

References 23 publications

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“…The second is in running, diving, jumping, and walking, the actor's body is dynamically captured through the human motion capture and recognition system, and the motion of the actor is guided and corrected by analysis of each joint movement of the human body. Action players can get better training results and create better results [6][7][8][9]. It can accurately collect the sports parameters of the athletes in real time and realize the analysis and recognition of the sports postures of the athletes.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Chu

2021

Journal of Sensors

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This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real-time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first-level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second-level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Chu

2021

Journal of Sensors

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“…The human actions of understanding and analyzing have received much research attention for multiple areas of applications, including human-robot interaction, surveillance, daily living, and video-based monitoring [91] . Lie groups have played an important role due to their properties.…”

Section: Application Of Lie Group Machine Learning To Image Processingmentioning

confidence: 99%

Survey on lie group machine learning

Mei¹,

Li²

2020

Big Data Min. Anal.

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Lie group machine learning is recognized as the theoretical basis of brain intelligence, brain learning, higher machine learning, and higher artificial intelligence. Sample sets of Lie group matrices are widely available in practical applications. Lie group learning is a vibrant field of increasing importance and extraordinary potential and thus needs to be developed further. This study aims to provide a comprehensive survey on recent advances in Lie group machine learning. We introduce Lie group machine learning techniques in three major categories: supervised Lie group machine learning, semisupervised Lie group machine learning, and unsupervised Lie group machine learning. In addition, we introduce the special application of Lie group machine learning in image processing. This work covers the following techniques: Lie group machine learning model, Lie group subspace orbit generation learning, symplectic group learning, quantum group learning, Lie group fiber bundle learning, Lie group cover learning, Lie group deep structure learning, Lie group semisupervised learning, Lie group kernel learning, tensor learning, frame bundle connection learning, spectral estimation learning, Finsler geometric learning, homology boundary learning, category representation learning, and neuromorphic synergy learning. Overall, this survey aims to provide an insightful overview of state-of-the-art development in the field of Lie group machine learning. It will enable researchers to comprehensively understand the state of the field, identify the most appropriate tools for particular applications, and identify directions for future research.

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“…Many researchers have spent their valuable time preparing some challenging datasets based on the skeleton of the human body for further experiments including NTU, MSR Action3D, Berkeley MHAD, HDM05, and UTD multimodal human action dataset (UTD-MHAD) datasets. Several ideas developed based on skeleton data [16][17][18][19][20][21][22][23][24][25][26][27][28][29] are used to separate action classes. In [16], J. Imran et al evaluated a method based on skeleton augmented data of 3D skeleton joints information using 3D transformations and designed a RNN-based BiGRU for the purpose of classification.…”

Section: Skeleton-based Action Recognitionmentioning

confidence: 99%

“…They described the skeleton joints as a tree where the center was considered the root node of the tree. Huynh-The et al [24] proposed a novel encoding method to transform skeleton information into image-based information called pose-transition feature and then into image representation for deep convolutional neural networks. Si et al [25] developed a hierarchical spatial reasoning network that receives information about each part, and the joints of each part, of the body using a graph neural network.…”

Section: Skeleton-based Action Recognitionmentioning

confidence: 99%

Deep Learning-Based Action Recognition Using 3D Skeleton Joints Information

2020

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Human action recognition has turned into one of the most attractive and demanding fields of research in computer vision and pattern recognition for facilitating easy, smart, and comfortable ways of human-machine interaction. With the witnessing of massive improvements to research in recent years, several methods have been suggested for the discrimination of different types of human actions using color, depth, inertial, and skeleton information. Despite having several action identification methods using different modalities, classifying human actions using skeleton joints information in 3-dimensional space is still a challenging problem. In this paper, we conceive an efficacious method for action recognition using 3D skeleton data. First, large-scale 3D skeleton joints information was analyzed and accomplished some meaningful pre-processing. Then, a simple straight-forward deep convolutional neural network (DCNN) was designed for the classification of the desired actions in order to evaluate the effectiveness and embonpoint of the proposed system. We also conducted prior DCNN models such as ResNet18 and MobileNetV2, which outperform existing systems using human skeleton joints information.

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Image representation of pose-transition feature for 3D skeleton-based action recognition

Cited by 79 publications

References 23 publications

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor

Survey on lie group machine learning

Deep Learning-Based Action Recognition Using 3D Skeleton Joints Information

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