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3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks
[article]
2020
arXiv
pre-print
Preserved Fulltext
Two stream, "twin" convolutional neural networks are used with 3D convolutions both on RGB data and optical flow. Actions are recognized by classification of temporal windows. ...
The paper addresses the problem of recognition of actions in video with low inter-class variability such as Table Tennis strokes. ...
Twin Spatio-Temporal Convolutional Neural Network -TSTCNN In order to perform action classification in videos, we use a two streams convolutional neural network (twin) with attention mechanism. ...
arXiv:2012.05342v1
fatcat:y4mqrd3q4bgdlnp54hmmztkmum
3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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Two stream, "twin" convolutional neural networks are used with 3D convolutions both on RGB data and optical flow. Actions are recognized by classification of temporal windows. ...
The paper addresses the problem of recognition of actions in video with low inter-class variability such as Table Tennis strokes. ...
Twin Spatio-Temporal Convolutional Neural Network -TSTCNN In order to perform action classification in videos, we use a two streams convolutional neural network (twin) with attention mechanism. ...
doi:10.1109/icpr48806.2021.9412742
fatcat:jwykfzvyyfhj5hy6hwphkkw3mi
Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021
[article]
2021
arXiv
pre-print
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The Sports Video task is part of the MediaEval 2021 benchmark. This task tackles fine-grained action detection and classification from videos. The focus is on recordings of table tennis games. ...
Running since 2019, the task has offered a classification challenge from untrimmed video recorded in natural conditions with known temporal boundaries for each stroke. ...
Fine grained sport action recognition with Twin spatio-temporal convolu-
tional neural networks. Multim. ...
arXiv:2112.11384v1
fatcat:5qidqoysgvfshoirktbcmlsedu
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition
[article]
2022
arXiv
pre-print
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3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy. ...
In the chapter we are interested in the classification of continuous video takes with repeatable actions, such as strokes of table tennis. ...
our solution to the problem of fined-grained action recognition in video with a 3D CNN we call TSTCNNa twin spatio-temporal CNN. ...
arXiv:2204.08460v1
fatcat:7ks2orf4m5ge5ggmafbeghbrsu
Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020
2020
MediaEval Benchmarking Initiative for Multimedia Evaluation
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This work presents a method for classifying table tennis strokes using spatio-temporal convolutional neural networks. ...
A three stream spatio-temporal convolutional neural network using combination of those modalities and 3D attention mechanisms is presented in order to perform classification. ...
Model architecture The model was similar to the Twin Spatio-Temporal Convolutional Neural Network -TSTCNN with attention mechanisms presented in [15] . ...
dblp:conf/mediaeval/MartinBMPM20
fatcat:kl3tz5ag6ragdkeyysjpwv2qga
HCMUS at MediaEval 2020: Ensembles of Temporal Deep Neural Networks for Table Tennis Strokes Classification Task
2020
MediaEval Benchmarking Initiative for Multimedia Evaluation
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In this task, we -the HCMUS Teamperform multiple experiments, which includes a combination of models such as SlowFast, Optical Flow, DensePose, R2+1, Channel-Separated Convolutional Networks, to classify ...
In total, we submit eight runs corresponding to five different models with different sets of hyperparameters in each of our models. ...
Twin Spatio-Temporal Convolutional Neural Networks (TSTCNN) In this task, we also use the Twin Spatio-Temporal Convolutional Neural Networks (TSTCNN) [10] and conduct experiments on it with our minor ...
dblp:conf/mediaeval/Nguyen-TruongCN20
fatcat:w4dyczo7gzfcbip2mqjmvycxxa
An overview of Human Action Recognition in sports based on Computer Vision
2022
Heliyon
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Human Action Recognition (HAR) is a challenging task used in sports such as volleyball, basketball, soccer, and tennis to detect players and recognize their actions and teams' activities during training ...
As an action that can occur in the sports field refers to a set of physical movements performed by a player in order to complete a task using their body or interacting with objects or other persons, actions ...
In [45] , a Twin Spatio-temporal Convolutional Neural Network, which takes as inputs an RGB image sequence and its computed Optical Flow, is proposed. ...
doi:10.1016/j.heliyon.2022.e09633
pmid:35706961
pmcid:PMC9189896
fatcat:5o4x4ywsanfcfo6wvkoc2g6lym
Sports Video Classification: Classification of Strokes in Table Tennis for MediaEval 2020
2020
MediaEval Benchmarking Initiative for Multimedia Evaluation
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Fine grained action classification has raised new challenges compared to classical action classification problem. ...
Sport video analysis is a very popular research topic, due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests, up to analysis of athletes' performances ...
Neural Network. ...
dblp:conf/mediaeval/MartinBMPMCM20
fatcat:m4cvhjdhvzfr5gbstx4qe6kvai
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 3612-3625 Learning Latent Global Network for Skeleton-Based Action Prediction. Learning Rich Part Hierarchies With Progressive Attention Networks for Fine-Grained Image Recognition. ...
., +, TIP 2020 15-28 A Context Knowledge Map Guided Coarse-to-Fine Action Recognition. Ji, Y., +, TIP 2020 2742-2752 A Spatio-Temporal Multi-Scale Binary Descriptor. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
An Efficient Spatio-Temporal Pyramid Transformer for Action Detection
[article]
2022
arXiv
pre-print
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To this end, we present an efficient hierarchical Spatio-Temporal Pyramid Transformer (STPT) for action detection, building upon the fact that the early self-attention layers in Transformers still focus ...
Specifically, we propose to use local window attention to encode rich local spatio-temporal representations in the early stages while applying global attention modules to capture long-term space-time dependencies ...
To date, the majority of action detection methods [66, 32, 38, 54, 12] are driven by 3D convolutional neural networks (CNNs), e.g., C3D [56] , I3D [10] , to encode video segment features from video ...
arXiv:2207.10448v1
fatcat:wfcaqo5idncqloio6nk2a5ooh4
A Fine Grainedresearch Over Human Action Recognition
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Further, a fine-grained survey is also accomplished under every phase based on the individual strategies ...
Unlike the earlier ones, this paper provides a detailed survey according to the basic working methodology of Human action recognition system. ...
Further, a fine-grained analysis is accomplished over the deep learning approaches with respect to the convolution dimension. ...
doi:10.35940/ijitee.a4677.119119
fatcat:tacsukuctjehde4vzub5gzvfqu
Learning Neural Textual Representations for Citation Recommendation
2021
2020 25th International Conference on Pattern Recognition (ICPR)
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3D Attention Mechanism for Fine-Grained Classification of Table
Tennis Strokes Using a Twin Spatio-Temporal Convolutional Neural
Networks
DAY 3 -Jan 14, 2021
Martins Camboim de Sá, Jáder;
Luis ...
Modeling in a
Spatio-Temporal Graph Convolutional Network for Action
Recognition
DAY 2 -Jan 13, 2021
Song, Siyang; Sanchez, Enrique;
Shen, Linlin; Valstar, Michel
589
Self-Supervised Learning ...
doi:10.1109/icpr48806.2021.9412725
fatcat:3vge2tpd2zf7jcv5btcixnaikm
Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis
[article]
2021
pre-print
Preserved Fulltext
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This paper proposes a fusion method of modalities extracted from video through a three-stream network with spatio-temporal and temporal convolutions for fine-grained action classification in sport. ...
The network consists of three branches with attention blocks. Features are fused at the latest stage of the network using bilinear layers. ...
The target application of our research is fine-grained action recognition in sports with the aim of improving athletes performance. ...
doi:10.1145/3475722.3482793
arXiv:2109.14306v1
fatcat:ttwu552ct5hdzalvnamyl2pt2e
Deep Learning for Visual Tracking: A Comprehensive Survey
[article]
2019
arXiv
pre-print
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visual tracking, network objective, network output, and the exploitation of correlation filter advantages. ...
First, the fundamental characteristics, primary motivations, and contributions of DL-based methods are summarized from six key aspects of: network architecture, network exploitation, network training for ...
Although convolutional neural networks (CNNs) have been dominant networks initially, the broad range of architectures such as Siamese neural networks (SNNs), recurrent neural networks (RNNs), auto-encoders ...
arXiv:1912.00535v1
fatcat:v5ikqi2cpbblhgtkiu6z6l5anq
Enhanced Video Classification System Using a Block-Based Motion Vector
2020
Information
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When shot level features and keyframe features, along with motion vectors, were used, 86% correct classification was achieved, which was comparable with the existing methods. ...
Convolutional neural networks (CNNs) are used [5] for large scale video classification. ...
In [6] , recurrent convolutional neural networks (RCNNs) were used for video classification tasks, which are good at learning relations from input sequences. ...
doi:10.3390/info11110499
fatcat:vlbz6mqu3jej7pnk3nq62u575m
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