Fall detection based on OpenPose and MobileNetV2 network

Gao, Mengqi; Li, Jiangjiao; Zhou, Dazheng; Zhi, Yumin; Zhang, Mingliang; Li, Bin

doi:10.1049/ipr2.12667

Cited by 13 publications

(6 citation statements)

References 33 publications

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“…In contrast, DL methods provide the ability to automatically learn features from data. Deep features for skeleton-based fall detection can be acquired by utilizing various type of NN such as 1D CNN [64], [76], RNN/LSTM/GRU [56]- [61], GCN [66], [87], [149], some modern networks (Transformer [56], BERT [68]) or combination of these architectures [57], [62], [63], [67]. Due to the variability of evaluation settings (discussed in Section IV-G1), it is challenging to determine the superior methods.…”

Section: ) Discussionmentioning

confidence: 99%

Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches

Hoang,

Lee,

Piran

et al. 2023

IEEE Access

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In the elderly population, falls are one of the leading causes of fatal and non-fatal injuries. Fall detection and early alarms play an important role in mitigating the negative effects of falls, especially given the growing proportion of the elderly population. Due to their non-intrusive nature, data availability, and low deployment costs, RGB videos have been used in many previous studies to detect falls. The RGB data, however, can be affected by background environment changes, resulting in non-recognition. To overcome these challenges, many researchers propose extracting skeleton data from RGB videos and using it for fall detection. Although there have been multiple surveys on fall detection, most of them focus on assessing fall detection systems using different kinds of sensors, and a comprehensive evaluation of skeleton-based fall detection in RGB videos is lacking. In this paper, we examine the most recent advances in skeleton-based fall detection in RGB videos, from handcrafted feature-based methods to advanced deep learning algorithms. Further, we present several skeleton-based fall detection techniques and their performance results on various benchmark datasets, along with challenges and future directions in this field.

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Section: ) Discussionmentioning

confidence: 99%

Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches

Hoang,

Lee,

Piran

et al. 2023

IEEE Access

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“…The utilization of the channel attention mechanism enhances the network's ability to focus on relevant and discriminative features, thus improving its performance in various computer vision tasks. It provides a mechanism for adaptively recalibrating the importance of different channels, enabling more effective information processing within CNNs (Gao et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Peng

2023

Front. Neurosci.

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IntroductionWith the rapid advancement of artificial intelligence (AI) technology, the protection of patient medical image privacy and security has become a critical concern in current research on image privacy protection. However, traditional methods for encrypting medical images have faced criticism due to their limited flexibility and inadequate security. To overcome these limitations, this study proposes a novel chaotic medical image encryption method, called AT-ResNet-CM, which incorporates the attention mechanism fused with the ResNet model.MethodsThe proposed method utilizes the ResNet model as the underlying network for constructing the encryption and decryption framework. The ResNet's residual structure and jump connections are employed to effectively extract profound information from medical images and expedite the model's convergence. To enhance security, the output of the ResNet model is encrypted using a logistic chaotic system, introducing randomness and complexity to the encryption process. Additionally, an attention mechanism is introduced to enhance the model's response to the region of interest within the medical image, thereby strengthening the security of the encrypted network.ResultsExperimental simulations and analyses were conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed method outperforms alternative models in terms of encryption effectiveness, as indicated by a horizontal correlation coefficient of 0.0021 and information entropy of 0.9887. Furthermore, the incorporation of the attention mechanism significantly improves the encryption performance, reducing the horizontal correlation coefficient to 0.0010 and increasing the information entropy to 0.9965. These findings validate the efficacy of the proposed method for medical image encryption tasks, as it offers enhanced security and flexibility compared to existing approaches.DiscussionIn conclusion, the AT-ResNet-CM method presents a promising solution to address the limitations of traditional encryption techniques in protecting patient medical images. By leveraging the attention mechanism fused with the ResNet model, the method achieves improved security and flexibility. The experimental results substantiate the superiority of the proposed method in terms of encryption effectiveness, horizontal correlation coefficient, and information entropy. The proposed method not only addresses the shortcomings of traditional methods but also provides a more robust and reliable approach for safeguarding patient medical image privacy and security.

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“…To enable iterative network structure and outcome prediction for each branch, a loss value is assigned at the end of each stage [48]. The upper and lower branch networks incorporate a loss function, which measures the discrepancy between the predicted and true values [49].…”

Section: Openpose-based Human Pose Estimation Algorithmmentioning

confidence: 99%

Motion Capture for Sporting Events Based on Graph Convolutional Neural Networks and Single Target Pose Estimation Algorithms

Duan

Liu³

et al. 2023

Applied Sciences

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Human pose estimation refers to accurately estimating the position of the human body from a single RGB image and detecting the location of the body. It serves as the basis for several computer vision tasks, such as human tracking, 3D reconstruction, and autonomous driving. Improving the accuracy of pose estimation has significant implications for the advancement of computer vision. This paper addresses the limitations of single-branch networks in pose estimation. It presents a top-down single-target pose estimation approach based on multi-branch self-calibrating networks combined with graph convolutional neural networks. The study focuses on two aspects: human body detection and human body pose estimation. The human body detection is for athletes appearing in sports competitions, followed by human body pose estimation, which is divided into two methods: coordinate regression-based and heatmap test-based. To improve the accuracy of the heatmap test, the high-resolution feature map output from HRNet is used for deconvolution to improve the accuracy of single-target pose estimation recognition.

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Fall detection based on OpenPose and MobileNetV2 network

Cited by 13 publications

References 33 publications

Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches

Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches

Chaotic medical image encryption method using attention mechanism fusion ResNet model

Motion Capture for Sporting Events Based on Graph Convolutional Neural Networks and Single Target Pose Estimation Algorithms

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