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action recognition Based on CSI Signal Using Improved Deep Residual Network Model

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1827-1851页

作者： Jian Zhao Shangwu Chong Liang Huang Xin Li Chen He Jian Jia School of Information Science and Technology Northwest UniversityXi’an710127China School of Mathematics Northwest UniversityXi’an710127China

In this paper,we propose an improved deep residual network model to recognize human *** data is composed of channel state information signals,which are continuous fine-grained *** replaced the traditional identity connection with the shrinking *** automatically adjusts the threshold of the action data signal,and filters out signals that are not related to the principal *** use the attention mechanism to improve the memory of the network model to the action signal,so as to better recognize the *** verify the validity of the experiment more accurately,we collected action data in two different *** experimental results show that the improved network model is much better than the traditional network in *** accuracy of recognition in complex places can reach 92.85%,among which the recognition rate of raising hands is up to 96%.We combine the improved residual deep network model with channel state information action data,and prove the effectiveness of our model for classification through experimental data.

关键词： action recognition residual deep network network model channel state information

action recognition and Detection Based on Deep Learning: A Comprehensive Summary

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Computers, Materials & Continua 2023年第10期77卷 1-23页

作者： Yong Li Qiming Liang Bo Gan Xiaolong Cui College of Information Engineering Engineering University of PAPXi’an710086China PAP of Heilongjiang Province Heihe DetachmentHeihe164300China National Key Laboratory of Science and Technology on Electromagnetic Energy Naval University of EngineeringWuhan430033China Joint Laboratory of Counter Terrorism Command and Information Engineering Engineering University of PAPXi’an710086China

action recognition and detection is an important research topic in computer vision,which can be divided into action recognition and action *** present,the distinction between action recognition and action detection is not clear,and the relevant reviews are not ***,this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this ***,according to the way that temporal and spatial features are extracted from the model,the commonly used models of action recognition are divided into the two stream models,the temporal models,the spatiotemporal models and the transformer models according to the *** this paper briefly analyzes the characteristics of the four models and introduces the accuracy of various algorithms in common data ***,from the perspective of tasks to be completed,action detection is further divided into temporal action detection and spatiotemporal action detection,and commonly used datasets are *** the perspectives of the twostage method and one-stage method,various algorithms of temporal action detection are reviewed,and the various algorithms of spatiotemporal action detection are summarized in ***,the relationship between different parts of action recognition and detection is discussed,the difficulties faced by the current research are summarized in detail,and future development was prospected。

关键词： action recognition action detection deep learning convolutional neural networks dataset

action recognition from Videos with Complex Background via Transfer Learning

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Journal of Donghua University(English Edition) 2010年第2期27卷 199-203页

作者：林贤明李绍滋张洪博刘姝 Department of Cognitive Science Fujian Key Laboratory of the Brain-like Intelligent SystemsXiamen University

Classifier learning methods commonly assume that the training data and the testing data are drawn from the same underlying distribution. However, in many practical situations, this assumption is violated. One example is the practical action videos with complex background and the universal human action databases of Kangliga Tekniska Hogskolan （KTH）. When training data are very scarce, supervised learning is difficult. However, it will cost lots of human and material resources to establish a labeled video set which includes a large amount of videos with complex backgrounds. In this paper, we propose an action recognition framework which uses transfer boosting learning algorithm. By using this algorithm, we can train an action recognition model fitting for most practical situations just relaying on the universal action video dataset and a tiny set of action videos with complex background. And the experiment results show that the performance is improved.

关键词： action recognition transfer adaboost learning maximum mutual information

ResLNet:deep residual LSTM network with longer input for action recognition

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Frontiers of Computer Science 2022年第6期16卷 41-49页

作者： Tian WANG Jiakun LI Huai-Ning WU Ce LI Hichem SNOUSSI Yang WU Institute of Artificial Intelligence Beihang UniversityBeijing100191China School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191China College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou730050China Institute Charles Delaunay-LM2S FRE CNRS 2019 University of Technology of TroyesTroyes10010France Institute for Research Initiatives Nara Institute of Science and TechnologyNara630-0192Japan

action recognition is an important research topic in video analysis that remains very *** recognition relies on learning a good representation of both spatial information(for appearance)and temporal information(for motion).These two kinds of information are highly correlated but have quite different properties,leading to unsatisfying results of both connecting independent models(e.g.,CNN-LSTM)and direct unbiased co-modeling(e.g.,3DCNN).Besides,a long-lasting tradition on this task with deep learning models is to just use 8 or 16 consecutive frames as input,making it hard to extract discriminative motion *** this work,we propose a novel network structure called ResLNet(Deep Residual LSTM network),which can take longer inputs(e.g.,of 64 frames)and have convolutions collaborate with LSTM more effectively under the residual structure to learn better spatial-temporal representations than ever without the cost of extra computations with the proposed embedded variable stride *** superiority of this proposal and its ablation study are shown on the three most popular benchmark datasets:Kinetics,HMDB51,and *** proposed network could be adopted for various features,such as RGB and optical *** to the limitation of the computation power of our experiment equipment and the real-time requirement,the proposed network is tested on the RGB only and shows great performance.

关键词： action recognition deep learning neural network

Two-Stream Temporal Convolutional Networks for Skeleton-Based Human action recognition

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Journal of Computer Science & Technology 2020年第3期35卷 538-550页

作者： Jin-Gong Jia Yuan-Feng Zhou Xing-Wei Hao Feng Li Christian Desrosiers Cai-Ming Zhang School of Software Shandong UniversityJinan 250101China Department of Software and IT Engineering University of QuebecMontreal H3C 3P8Canada

With the growing popularity of somatosensory interaction devices,human action recognition is becoming attractive in many application ***-based action recognition is effective because the skeleton can represent the position and the structure of key points of the human *** this paper,we leverage spatiotemporal vectors between skeleton sequences as input feature representation of the network,which is more sensitive to changes of the human skeleton compared with representations based on distance and angle *** addition,we redesign residual blocks that have different strides in the depth of the network to improve the processing ability of the temporal convolutional networks(TCNs)for long time dependent *** this work,we propose the two-stream temporal convolutional networks(TSTCNs)that take full advantage of the inter-frame vector feature and the intra-frame vector feature of skeleton sequences in the spatiotemporal *** framework can integrate different feature representations of skeleton sequences so that the two feature representations can make up for each other’s *** fusion loss function is used to supervise the training parameters of the two branch *** on public datasets show that our network achieves superior performance and attains an improvement of 1.2%over the recent GCN-based(BGC-LSTM)method on the NTU RGB+D dataset.

关键词： skeleton action recognition temporal convolutional network(TCN) vector feature representation neural network

Segment differential aggregation representation and supervised compensation learning of ConvNets for human action recognition

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Science China(Technological Sciences) 2024年第1期67卷 197-208页

作者： REN ZiLiang ZHANG QieShi CHENG Qin XU ZhenYu YUAN Shuai LUO DeLin School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin 541004China Department of the mechanical engineering Erlangen-Nuremberg UniversityErlangen 91508Germany School of Aerospace Engineering Xiamen UniversityXiamen 361102China

With more multi-modal data available for visual classification tasks,human action recognition has become an increasingly attractive ***,one of the main challenges is to effectively extract complementary features from different modalities for action *** this work,a novel multimodal supervised learning framework based on convolution neural networks(Conv Nets)is proposed to facilitate extracting the compensation features from different modalities for human action *** on information aggregation mechanism and deep Conv Nets,our recognition framework represents spatial-temporal information from the base modalities by a designed frame difference aggregation spatial-temporal module(FDA-STM),that the networks bridges information from skeleton data through a multimodal supervised compensation block(SCB)to supervise the extraction of compensation *** evaluate the proposed recognition framework on three human action datasets,including NTU RGB+D 60,NTU RGB+D 120,and *** results demonstrate that our model with FDA-STM and SCB achieves the state-of-the-art recognition performance on three benchmark datasets.

关键词： action recognition segment frame difference aggregation supervised compensation learning ConvNets

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Skeleton-based Violation action recognition Method for Safety Supervision in Operation Field of Distribution Network Based on Graph Convolutional Network

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2179-2187页

作者： Bo Wang Fuqi Ma Rong Jia Peng Luo Xuzhu Dong the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China the School of Electrical Engineering Xi’an University of TechnologyXi’an 710054China

Safety accidents in the operation field of the distribution network often occur,which seriously endanger the safety and lives of *** identification methods for safety risk can identify static safety risks,such as no-helmet,no-safety gloves,etc.,but fail to identify risks in the dynamic actions of ***,this paper proposes a skeletonbased violation action-recognition method for supervision of safety during operations in a distribution network,i.e.,based on spatial temporal graph convolutional network(STGCN)and key joint attention module(KJAM),which can implement dynamic violation behavior recognition of *** this method,the human posture estimation method,***-Person Pose Estimation,is utilized to extract the skeleton information of operators during operations,and to construct an undirected graph,which reflects the movement and posture of the human ***,the STGCN is utilized to identify actions of operators that can lead to dynamic *** addition,the KJAM captures important joint information of *** effectiveness and superiority of the proposed method are verified in comparison to other action recognition *** experimental results show that the proposed method has higher recognition accuracy for common violations collected at the actual operation site of the distribution network and shows a strong generalization ability,which can be applied to the video monitoring system of field operations to reduce the occurrence of safety accidents.

关键词： action recognition graph convolutional network power safety risk power vision safety supervision skeleton information

action recognition from arbitrary views using 3D-key-pose set

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Frontiers of Electrical and Electronic Engineering in China 2012年第2期7卷 224-241页

作者： Junxia GU Xiaoqing DING Shenjing WANG Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Recovering three-dimensional （3D） human pose sequence from arbitrary view is very difficult, due to loss of depth information and self-occlusion. In this paper, view-independent 3D-key-pose set is selected from 3D action samples, for the purpose of representing and recognizing those same actions from a single or few cameras without any restriction of the relative orientations between cameras and subjects. First, 3D-key-pose set is selected from the 3D human joint sequences of 3D training action samples that are built from multiple viewpoints. Second, 3D key pose sequence, which matches best with the observation sequence, is selected from the 3D-key- pose set to represent the observation sequence of arbitrary view. 3D key pose sequence contains many discriminative view-independent key poses but cannot accurately describe pose of every frame in the observation sequence. Considering the above reasons, pose and dynamic of action are modeled respectively in this paper. Exemplar-based embedding and probability of unique key pose are applied to model pose property. Complementary dynamic feature is extracted to model these actions that share the same poses but have different dynamic features. Finally, these action models are fused to recognize observation sequence from a single or few cameras. Effectiveness of the proposed approach is demonstrated with experiments on IXMAS dataset.

关键词： Keywords action representation action recognition 3D-key-pose set 3D key pose sequence action models fusion

Learning a discriminative mid-level feature for action recognition

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Science China(Information Sciences) 2014年第5期57卷 195-207页

作者： LIU CuiWei PEI MingTao WU XinXiao KONG Yu JIA YunDe Beijing Laboratory of Intelligent Information Technology School of Computer ScienceBeijing Institute of Technology

In this paper,we address the problem of recognizing human actions from *** of the existing approaches employ low-level features(e.g.,local features and global features)to represent an action ***,algorithms based on low-level features are not robust to complex environments such as cluttered background,camera movement and illumination ***,we propose a novel random forest learning framework to construct a discriminative and informative mid-level feature from low-level features of densely sampled 3D *** cuboid is classified by the corresponding random forests with a novel fusion scheme,and the cuboid’s posterior probabilities of all categories are normalized to generate a *** that,we obtain our mid-level feature by concatenating histograms of all the *** a single low-level feature is not enough to capture the variations of human actions,multiple complementary low-level features(i.e.,optical flow and histogram of gradient 3D features)are employed to describe 3D ***,temporal context between local cuboids is exploited as another type of low-level *** above three low-level features(i.e.,optical flow,histogram of gradient 3D features and temporal context)are effectively fused in the proposed learning ***,the mid-level feature is employed by a random forest classifier for robust action *** on the Weizmann,UCF sports,Ballet,and multi-view IXMAS datasets demonstrate that out mid-level feature learned from multiple low-level features can achieve a superior performance over state-of-the-art methods.

关键词： action recognition mid-level feature feature fusion temporal context