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Cascade Attention-based Spatial-temporal Convolutional Neural Network for Motion Image Posture Recognition

Shuqi Zhang Shuqi Zhang
2022 Diànnǎo xuékān  
Firstly, the convolutional neural network is used to model the time sequence relationship in the video, so as to capture the spatial-temporal information in the video efficiently.  ...  <p>The traditional motion posture recognition methods cannot capture the temporal relationship in a video sequence, which leads to the problem that the recognition effect of time-dependent behaviors is  ...  [14] proposed a Temporal Shift Module (TSM), which used two-dimensional convolutional neural network to extract Temporal sequence information in videos, but it reduced the learning ability of spatial  ... 
doi:10.53106/199115992022023301003 fatcat:4vacrjbdsjhadjr6u7bjjkqsue

Compressive sensing based privacy for fall detection [article]

Ronak Gupta, Prashant Anand, Santanu Chaudhury, Brejesh Lall, Sanjay Singh
2020 arXiv   pre-print
, rather than video sequence as input, as in the case of I3D convolutional neural network.  ...  The proposed architecture is a custom version of Inflated 3D (I3D) architecture, that takes compressed measurements of video sequence as spatio-temporal input, obtained from compressive sensing framework  ...  as input, as in the case of I3D convolutional neural networks.  ... 
arXiv:2001.03463v1 fatcat:hvgu32hipvau3b5bymtd3nuia4

Ensemble Three-Stream RGB-S Deep Neural Network for Human Behavior Recognition Under Intelligent Home Service Robot Environments

Yeong-Hyeon Byeon, Dohyung Kim, Jaeyeon Lee, Keun-Chang Kwak
2021 IEEE Access  
The first stream classifies behaviors in videos using a convolutional neural network (CNN) based on a pre-trained ResNet101 model, which uses two-dimensional (2D) sequence images of actions as its input  ...  Finally, a large-scale database for behavior recognition in videos, known as ETRI-Activity3D, is used in this study to verify the performance of the proposed deep neural network.  ...  The method for designing a deep neural network that classifies behavior recognition in videos is summarized as follows: [Step1] Use a pre-trained CNN such as GoogLeNet to convert the videos into a sequence  ... 
doi:10.1109/access.2021.3077487 fatcat:su233ecbhfdrlanjs5ysugss3y

Automated Bridge Component Recognition using Video Data [article]

Yasutaka Narazaki, Vedhus Hoskere, Tu A. Hoang, Billie F. Spencer Jr
2018 arXiv   pre-print
In particular, single-frame image processing techniques, such as convolutional neural networks (CNNs), are not expected to identify structural components accurately when the image is a close-up view, lacking  ...  Then, convolutional Neural Networks (CNNs) with recurrent architectures are designed and applied to implement the automated bridge component recognition task.  ...  Neural networks with recurrent architectures have been proposed as effective methods for modeling a sequence from collected data (a video is a sequence of images).  ... 
arXiv:1806.06820v2 fatcat:xcbnkm6w4vcrhicuu3neavkfvy

A Novel Approach for Robust Multi Human Action Recognition and Summarization based on 3D Convolutional Neural Networks [article]

Noor Almaadeed, Omar Elharrouss, Somaya Al-Maadeed, Ahmed Bouridane, Azeddine Beghdadi
2021 arXiv   pre-print
This is followed by an analysis of each sequence to detect and recognize the corresponding actions using 3D convolutional neural networks (3DCNNs).  ...  Human actions in videos are 3D signals. However, there are a few methods available for multiple human action recognition.  ...  In order to prepare data for convolutional neural networks for training, we isolated human bodies in the video to extract a sequence or clip of body motions during their action and their presence in the  ... 
arXiv:1907.11272v4 fatcat:gxg77wheffdqdguo52zhwvkppi

Gesture Recognition Based on 3D Human Pose Estimation and Body Part Segmentation for RGB Data Input

Ngoc-Hoang Nguyen, Tran-Dac-Thinh Phan, Guee-Sang Lee, Soo-Hyung Kim, Hyung-Jeong Yang
2020 Applied Sciences  
This paper presents a novel approach for dynamic gesture recognition using multi-features extracted from RGB data input.  ...  Most of the challenges in gesture recognition revolve around the axis of the presence of multiple actors in the scene, occlusions, and viewpoint variations.  ...  The development of deep learning methods based on a convolution neural network (CNN) and recurrent neural network (RNN) or long short-term memory network (LSTM) have achieved positive results in handling  ... 
doi:10.3390/app10186188 fatcat:sdygawbngncjtinuek225vgzoy

Aided Evaluation of Motion Action Based on Attitude Recognition

Qi Wang, Qing-Ming Wang, Le Sun
2022 Journal of Healthcare Engineering  
convolution neural network and video processing technology to create an auxiliary evaluation system of sports movements, which can obtain accurate data and help people interact with each other, so as to  ...  BP neural network can be as high as 96.4%; the correct recognition rate of the attitude recognition method based on this paper can be as high as 98.7%, which is 2.3% higher than the previous method.  ...  It can effectively detect the 2D action posture of a single person or multiple people in the video image to be detected.  ... 
doi:10.1155/2022/8388325 pmid:35310175 pmcid:PMC8926528 fatcat:6vw56yuvnfhbhif5r6dmftybhm

First-person activity recognition with C3D features from optical flow images

Asamichi Takamine, Yumi Iwashita, Ryo Kurazume
2015 2015 IEEE/SICE International Symposium on System Integration (SII)  
neural network for videos, called C3D (Convolutional 3D), was proposed.  ...  Generally CNN / C3D features are extracted directly from original images / videos with pre-trained convolutional neural network, since the network was trained with images / videos.  ...  ACKNOWLEDGMENTS This work is supported by a Grant-in-Aid for Exploratory Research (26630099).  ... 
doi:10.1109/sii.2015.7405050 dblp:conf/sii/TakamineIK15 fatcat:4k2cbnnye5f3fgey54inatorty

Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks

Nicolae-Catalin Ristea, Liviu Cristian Dutu, Anamaria Radoi
2019 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD)  
In this paper, we propose a system that is able to recognize emotions with a high accuracy rate and in real time, based on deep Convolutional Neural Networks.  ...  From a visual point of view, a human emotion can be recognized by analyzing the facial expression of the person.  ...  , such as a convolutional neural network, as detailed below.  ... 
doi:10.1109/sped.2019.8906538 dblp:conf/sped/RisteaDR19 fatcat:ivjhman7ybfntpv2akmvfaqsua

2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30

2020 IEEE transactions on circuits and systems for video technology (Print)  
., see Sepas-Moghaddam, A., TCSVT Dec. 2020 4496-4512 Hassanpour, H., see Khosravi, M.H., TCSVT Jan. 2020 48-58 Hatzinakos, D., see 2900-2916 Hayat, M., see 2900-2916 He, C., Hu, Y., Chen, Y., Fan  ...  Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network.  ...  ., +, TCSVT Oct. 2020 3714-3726 A Blind Image Quality Assessment Using a Deep Bilinear Convolutional Neural Network.  ... 
doi:10.1109/tcsvt.2020.3043861 fatcat:s6z4wzp45vfflphgfcxh6x7npu

Deep Personality Trait Recognition: A Survey

Xiaoming Zhao, Zhiwei Tang, Shiqing Zhang
2022 Frontiers in Psychology  
Then, we review the principles and recent advances of typical deep learning techniques, including deep belief networks (DBNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs)  ...  Motivated by the great success of deep learning methods in various tasks, a variety of deep neural networks have increasingly been employed to learn high-level feature representations for automatic personality  ...  of the following keywords: "personality trait recognition, " "personality computing, " "deep learning, " "deep belief networks, " "convolutional neural networks, " "recurrent neural networks, " "long  ... 
doi:10.3389/fpsyg.2022.839619 pmid:35645923 pmcid:PMC9136483 fatcat:5eh2ohzjwff5jb4yjn6rzrw5ye

Video person reidentification based on neural ordinary differential equations and graph convolution network

Li-qiang Zhang, Long-yang Huang, Xiao-li Duan
2021 EURASIP Journal on Advances in Signal Processing  
In order to make full use of the continuity of video data on the time line and the unstructured relationship of features, a video person reidentification algorithm combining the neural ordinary differential  ...  equation with the graph convolution network is proposed in this paper.  ...  In this respect, the mainstream neural network has been able to obtain a high recognition rate.  ... 
doi:10.1186/s13634-021-00747-1 fatcat:vxl3awf5yjgbni6nzbbum3wm6i

Parallel Sequence-Channel Projection Convolutional Neural Network for EEG-Based Emotion Recognition

Lili Shen, Wei Zhao, Yanan Shi, Tianyi Qin, Bingzheng Liu
2020 IEEE Access  
In recent years, convolutional neural network (CNN) [16] has become prevalent in EEG emotion recognition.  ...  To address these challenges, a parallel sequence-channel projection convolutional neural network (PSCP-Net) is proposed in this paper.  ... 
doi:10.1109/access.2020.3039542 fatcat:s5lfw6uybvcw5cyymub22axa5q

A Multi-Stream Convolutional Neural Network Framework for Group Activity Recognition [article]

Sina Mokhtarzadeh Azar, Mina Ghadimi Atigh, Ahmad Nickabadi
2018 arXiv   pre-print
In this work, we present a framework based on multi-stream convolutional neural networks (CNNs) for group activity recognition.  ...  Each stream has two branches to predict the group activity based on person and scene level representations.  ...  When using multi-stream convolutional networks for group activity, a challenge is how to model temporal stream for multiple individuals in the input video.  ... 
arXiv:1812.10328v1 fatcat:5yulxfy5tzew5bwvyrnina3d3u

Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields [article]

Behzad Hasani, Mohammad H. Mahoor
2017 arXiv   pre-print
In this paper, we propose a two-part network consisting of a DNN-based architecture followed by a Conditional Random Field (CRF) module for facial expression recognition in videos.  ...  Recently Deep Neural Networks (DNN) have shown to outperform traditional methods in visual object recognition.  ...  After training this network, we use its feature map in the second part of the network which is a linear chain Conditional Random Field (CRF) for sequence labeling.  ... 
arXiv:1703.06995v2 fatcat:zhxg3xpx7jh7bpwu4a4hpqnvye
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