An effectual classical dance pose estimation and classification system employing Convolution Neural Network –Long ShortTerm Memory (CNN-LSTM) network for video sequences

Rani, Challapalli Jhansi; Devarakonda, Nagaraju

doi:10.1016/j.micpro.2022.104651

Cited by 12 publications

(11 citation statements)

References 16 publications

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“…The convolutional layer is designed to perform convolution and activation operations on the input data and produce feature maps [29]. The mathematic procedure of convolution in layer l is presented in [33], as shown below:…”

Section: Cnn-lstm Hybrid Modelmentioning

confidence: 99%

CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines

Shang

Zhang

Tang

et al. 2023

Sensors

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The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challenging due to the complexity of the operational conditions and environment in the pipelines. Machine learning approaches in recent years, including convolutional neural networks (CNN) and long short-term memory (LSTM), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. In this study, a CNN-LSTM hybrid model was utilized for decoding ultrasonic guided waves for damage detection in metallic pipelines, and twenty-nine features were extracted as input to classify different types of defects in metallic pipes. The prediction capacity of the CNN-LSTM model was assessed by comparing it to those of CNN and LSTM. The results demonstrated that the CNN-LSTM hybrid model exhibited much higher accuracy, reaching 94.8%, as compared to CNN and LSTM. Interestingly, the results also revealed that predetermined features, including the time, frequency, and time–frequency domains, could significantly improve the robustness of deep learning approaches, even though deep learning approaches are often believed to include automated feature extraction, without hand-crafted steps as in shallow learning. Furthermore, the CNN-LSTM model displayed higher performance when the noise level was relatively low (e.g., SNR = 9 or higher), as compared to the other two models, but its prediction dropped gradually with the increase of the noise.

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Section: Cnn-lstm Hybrid Modelmentioning

confidence: 99%

CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines

Shang

Zhang

Tang

et al. 2023

Sensors

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“…CNN has also been successfully used for capture posture detection [ 95 ]. Rani et al [ 96 ] adopted the lightweight network of convolution neural network-long short-term memory (CNN-LSTM) for classical dance pose estimation and classification. Zhu et al [ 97 ] proposed a two-flow RGB-D faster R-CNN algorithm to achieve automatic posture recognition of sows, which applied the feature level fusion strategy.…”

Section: Deep Neural Network-based Approachmentioning

confidence: 99%

“…Deep residual networks address network degradation using residual learning with identity connections [ 99 ]. CNN-LSTM provides solutions to complex problems with large amounts of data [ 96 ]. Since target tracking methods based on traditional CNN and correlation filters are usually limited to feature extraction with scale invariance, multi-scale spatio-temporal residual network (MSST-ResNet) can be used to realize multi-scale feature and spatio-temporal interaction between the flows of spatial and time [ 100 ], which is also regarded as an extension of residual network architecture.…”

Section: Deep Neural Network-based Approachmentioning

confidence: 99%

A Survey on Artificial Intelligence in Posture Recognition

Jiang¹,

Hu²,

Wang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Over the years, the continuous development of new technology has promoted research in the field of posture recognition and also made the application field of posture recognition have been greatly expanded. The purpose of this paper is to introduce the latest methods of posture recognition and review the various techniques and algorithms of posture recognition in recent years, such as scale-invariant feature transform, histogram of oriented gradients, support vector machine (SVM), Gaussian mixture model, dynamic time warping, hidden Markov model (HMM), lightweight network, convolutional neural network (CNN). We also investigate improved methods of CNN, such as stacked hourglass networks, multi-stage pose estimation networks, convolutional pose machines, and high-resolution nets. The general process and datasets of posture recognition are analyzed and summarized, and several improved CNN methods and three main recognition techniques are compared. In addition, the applications of advanced neural networks in posture recognition, such as transfer learning, ensemble learning, graph neural networks, and explainable deep neural networks, are introduced. It was found that CNN has achieved great success in posture recognition and is favored by researchers. Still, a more in-depth research is needed in feature extraction, information fusion, and other aspects. Among classification methods, HMM and SVM are the most widely used, and lightweight network gradually attracts the attention of researchers. In addition, due to the lack of 3D benchmark data sets, data generation is a critical research direction.

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“…The role of convolutional layer is to create characteristic maps by performing convolution and activation operations on the input vector (Zhang et al, 2023). Presented in Rani and Devarakonda (2022), the mathematical formula of convolution layer is as follows:…”

Section: Fusion Model Of Cnn and Lstm (Cnn-lstm)mentioning

confidence: 99%

“…The average pooling layer, which comes after the convolutional layer, is to reduce the spatial dimensions (width and height) of the feature maps while retaining their depth (number of channels) and speed up model calculation. The mathematical representation of the pooling procedure is shown as in Rani and Devarakonda (2022):…”

Section: Fusion Model Of Cnn and Lstm (Cnn-lstm)mentioning

confidence: 99%

Signal Process of Ultrasonic Guided Wave for Damage Detection of Localized Defects in Plates: From Shallow Learning to Deep Learning

Shang,

Zhang,

Tang

et al. 2023

JDSIS

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Ultrasonic-guided waves are one of the non-destructive inspection techniques used in structural health monitoring (SHM) for localized damage detection. Propagation, scattering, and dispersion of the Lamb waves Even though the great progress in the monitoring technology of the Lamb wave, the detection, identification, and quantification of structure damage still face challenges because of the complexity of the process in the propagation, scattering and dispersion of the Lamb wave. Machine learning methods, including shallow learning (support vector machine (SVM)) and deep learning methods (convolutional neural networks (CNN) and long short-term memory (LSTM)), in recent years have brought revolutionary opportunities for decoding the information of the Lamb wave. Therefore, the methodology structure was proposed from dataset collection, data preprocessing (including feature extraction, feature combination, and feature transformation), data training, and classification. Two different cases of damage types and damage sizes were designed in a COMSOL environment. The shallow learning method of the SVM model and the deep learning method of the CNN-LSTM model were compared with the defined time-series features and transformed images. The results showed that both shallow learning methods and deep learning methods can be used in the application of signal classification, while the deep learning method of CNN-LSTM exhibited higher accuracy in image classification, as compared to the SVM. The robustness of the proposed models has also been verified under noise interference. The results demonstrated that the deep learning architecture of CNN-LSTM has the potential to attain greater precision because of better feature extraction and processing ability than the shallow learning model of SVM. In addition, the performance of signal classification and image classification of SVM and CNN-LSTM models dramatically decreased as the noise levels increased.

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An effectual classical dance pose estimation and classification system employing Convolution Neural Network –Long ShortTerm Memory (CNN-LSTM) network for video sequences

Cited by 12 publications

References 16 publications

CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines

CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines

A Survey on Artificial Intelligence in Posture Recognition

Signal Process of Ultrasonic Guided Wave for Damage Detection of Localized Defects in Plates: From Shallow Learning to Deep Learning

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