Study on the Identification Method of Human Upper Limb Flag Movements Based on Inception-ResNet Double Stream Network

Zhong, Yue; Luo, Jiancheng; Fang, Husheng; Shao, Faming; Ranzhi, Zhou

doi:10.1109/access.2020.3047455

Cited by 3 publications

(2 citation statements)

References 37 publications

(41 reference statements)

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“…The traditional method of behavior recognition is to extract local high-dimensional visual features of video regions, then combine them into fixed size video level descriptions, and finally use a classifier for final prediction [1]. Based on deep learning methods, single stream method [2] and double stream method [3].…”

Section: Introductionmentioning

confidence: 99%

Behavior recognition based on enhanced human pose estimation

zifan,

jun

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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In public places, rapid and accurate behavior recognition helps to monitor the occurrence of dangerous events. With the rise of neural networks, many methods have been developed to handle behavior recognition problems. Unlike target recognition, behavior recognition requires not only analyzing the spatial dependencies of the target, but also analyzing the historical information of target changes. We have inherited the traditional dual stream recognition method, in order to better extract spatial and temporal features in consistency, strengthen the contextual information extraction ability and channel fusion. In this paper, we propose a parallel convolutional network (MFC network) for extracting richer contextual information from dual flow branches. In addition, we propose a spatiotemporal modal interaction module (ST block) to improve the robustness of the network. The experimental results on datasets HMDB-51 and UFC-101 show that the method is effective.

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Section: Introductionmentioning

confidence: 99%

Behavior recognition based on enhanced human pose estimation

zifan,

jun

2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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“…Comparison of different modelsTo assess the performance and effectiveness of the proposed scSE-ResNet-50-TSCNN model in terms of accuracy, this paper conducted a comprehensive comparative analysis involving several other models. The models subjected to comparison include the following: a single force signal branch network without attention mechanism optimization, Force-ResNet-50[27]; a single image branch network without attention mechanism optimization, Image-ResNet-50[28]; a two-stream network without attention mechanism optimization, ResNet-50-TSCNN[29,30]; a single force signal branch network optimized with attention mechanism, Force-scSE-ResNet-50; and a single image branch network optimized with attention mechanism, Image-scSE-ResNet-50. All…”

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confidence: 99%

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

Nie,

Guo,

Lou

et al. 2024

Meas. Sci. Technol.

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In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE(Concurrent Spatial and Channel Squeeze and Excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and Continuous Wavelet Transform (CWT). Concurrently, the images undergo processing using Contrast Limited Adaptive Histogram Equalization (CLAHE) and the Structural Similarity Index Method (SSIM), allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer (CFRP) and Ti-6Al-4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model's effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

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Research on Multimodal Human Behavior Recognition Based on Double Flow Network

Bao

2021

2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)

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Study on the Identification Method of Human Upper Limb Flag Movements Based on Inception-ResNet Double Stream Network

Cited by 3 publications

References 37 publications

Behavior recognition based on enhanced human pose estimation

Behavior recognition based on enhanced human pose estimation

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

Research on Multimodal Human Behavior Recognition Based on Double Flow Network

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