[Retracted] Human Skeleton Detection and Extraction in Dance Video Based on PSO‐Enabled LSTM Neural Network

Li, Dingxin

doi:10.1155/2021/2545151

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…The skeleton data is extracted from input video using the skeleton extraction technique [22], which involves capturing frames with various human joint coordinates. This data is used to evaluate a specific human's skeleton, as it contains positional data on The DDSTGCNN uses skeleton data to create higher-level feature mappings and joint positional trajectory on graph nodes.…”

Section: Skeleton Data Processing By Dynamic Dense Spatio-temporal Gr...mentioning

confidence: 99%

Recognition of Indian Continuous Sign Language Using Spatio-Temporal Hybrid Cue Network

2023

IJIES

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People who are deaf or hard of hearing typically communicate primarily through sign language (SL). A complication arises when a deaf\mute person tries to use SL with someone who aren't familiar with it. The use of modern technological advancements will be useful in this situation. In recent years, prediction of SLs has been greatly improved by deep learning (DL) techniques. Amongst, a spatial-temporal multi-cue (STMC) network combines a spatial multi-cue (SMC) module and temporal multi-cue (TMC) to learn spatial and temporal encoding of multi-cue features like full-frame, hands, face, and stance. While it outperforms single-cue algorithms on large SL datasets, it requires more time to pre-process the dataset. Also, annotating key points is required for the complete training of the STMC network. This article presents a spatio-temporal hybrid cue network (STHCN) using dynamic dense spatiotemporal graph convolutional neural network (DDSTGCNN) and VGG11+1D-CNN+BLSTM feature extractor network to address the fore-mentioned issues of ISL recognition and translation tasks. Initially, the skeleton and full frame data is extracted from the input video. The DDSTGCNN is a combination of dense-GCNN (DGCNN) and dynamic spatial-temporal convolution network module (DSTCNM) which learns spatial features temporal features of skeleton data. VGG11+1D-CNN+BLSTM is used to extract the features from full frame data. Then, the extracted features are fed into a bidirectional long-short term memory (BLSTM) encoder, connectionist temporal classification (CTC), and self-attention based LSTM (SA-LSTM) decoders for sequence learning and inference. Finally, The CTC and SA-LSTM predict ISL from input videos and sentences for ISL detection and translation. Finally, an experiment results reveal that the STHCN model achieves 93.65% accuracy in detecting the ISL.

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Section: Skeleton Data Processing By Dynamic Dense Spatio-temporal Gr...mentioning

confidence: 99%

Recognition of Indian Continuous Sign Language Using Spatio-Temporal Hybrid Cue Network

2023

IJIES

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“…The global optimal position gbest and local optimal position pbest are determined by the initial Fitness value of the particles [ 33 ], and they are regarded as the historical optimal positions. According to the speed and position of the updated particles, the corresponding particle Fitness value is calculated and then updated to improve the prediction accuracy.…”

Section: Collision Risk Prediction Modelmentioning

confidence: 99%

“…The termination condition is judged to be satisfied (the Fitness value of particles tends to be stable or reaches the maximum number of iterations with iteration [ 33 ]). If the termination condition is satisfied, optimal parameters are assigned to PSO-LSTM model; otherwise, step (4) is returned.…”

Section: Collision Risk Prediction Modelmentioning

confidence: 99%

The Application of Automatic Identification System Information and PSO-LSTM Neural Network in CRI Prediction

Zhou

Xiao

et al. 2022

Computational Intelligence and Neuroscience

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Considering that collision accidents happen sometimes, it is necessary to predict the collision risk to ensure navigation safety. With the information construction in maritime and the popularity of automatic identification system application, it is more convenient to obtain ship navigation dynamics. How to obtain ship encounter dynamic parameters through automatic identification system information, assess ship collision risk, find out dangerous target ships, and give early warning and guarantee for ship navigation safety, is a problem that scholars have been studying. As an index to measure the degree of ship collision risk, CRI, namely, collision risk index, is usually obtained by calculating ship encounter parameters and comprehensive analysis. There are many factors that affect CRI, and the values of many parameters depend on expert judgment. The corresponding CRI has nonlinear and complex characteristics, which is highly correlated with the time sequence. In order to enhance the prediction accuracy and efficiency, PSO-LSTM neural network is applied in the paper to predict CRI. Experiments show that PSO-LSTM neural network can effectively predict collision risk and provide a reference for navigation safety.

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“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

mentioning

confidence: 99%

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

mentioning

confidence: 99%