Xu et al., 2021 - Google Patents
Motion analysis for dragon boat athlete using deep neural networksXu et al., 2021
- Document ID
- 1656934410817067149
- Author
- Xu C
- Wu Z
- Wu B
- Tan Y
- Publication year
- Publication venue
- Bio-Inspired Computing: Theories and Applications: 15th International Conference, BIC-TA 2020, Qingdao, China, October 23-25, 2020, Revised Selected Papers 15
External Links
Snippet
In the training of sports, video-based motion analysis is important to automatically capture the action of trainees and provide training suggestions. Focusing on the dragon boating which mainly involves periodic rowing actions, this paper proposes a motion analysis …
- 238000004458 analytical method 0 title abstract description 30
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00228—Detection; Localisation; Normalisation
- G06K9/00248—Detection; Localisation; Normalisation using facial parts and geometric relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00006—Acquiring or recognising fingerprints or palmprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
- G06T13/20—3D [Three Dimensional] animation
- G06T13/40—3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yadav et al. | Real-time Yoga recognition using deep learning | |
Sincan et al. | Autsl: A large scale multi-modal turkish sign language dataset and baseline methods | |
Jacob et al. | Facial action unit detection with transformers | |
Zhao et al. | Single image action recognition using semantic body part actions | |
Yang et al. | Faceness-net: Face detection through deep facial part responses | |
Chen et al. | Adversarial posenet: A structure-aware convolutional network for human pose estimation | |
Tang et al. | Multi-stream deep neural networks for rgb-d egocentric action recognition | |
Zhu | Computer Vision‐Driven Evaluation System for Assisted Decision‐Making in Sports Training | |
Avola et al. | Deep temporal analysis for non-acted body affect recognition | |
Martínez-González et al. | Efficient convolutional neural networks for depth-based multi-person pose estimation | |
CN110443189A (en) | Face character recognition methods based on multitask multi-tag study convolutional neural networks | |
Shen et al. | Exemplar-based human action pose correction | |
Cui et al. | Deep learning based advanced spatio-temporal extraction model in medical sports rehabilitation for motion analysis and data processing | |
Sciortino et al. | On the estimation of children’s poses | |
Yan et al. | A review of basketball shooting analysis based on artificial intelligence | |
Pang et al. | Dance video motion recognition based on computer vision and image processing | |
Liu et al. | Action recognition for sports video analysis using part-attention spatio-temporal graph convolutional network | |
Guo et al. | PhyCoVIS: A visual analytic tool of physical coordination for cheer and dance training | |
Chen et al. | Hierarchical posture representation for robust action recognition | |
Hachaj et al. | Human actions recognition on multimedia hardware using angle-based and coordinate-based features and multivariate continuous hidden Markov model classifier | |
Xu et al. | Motion analysis for dragon boat athlete using deep neural networks | |
Wang et al. | A novel local feature descriptor based on energy information for human activity recognition | |
Wang | Neural Network‐Oriented Big Data Model for Yoga Movement Recognition | |
Acı et al. | Accuracy Detection in Some Sports Training Using Computer Vision and Deep Learning Techniques | |
Al Shami | Generating Tennis Player by the Predicting Movement Using 2D Pose Estimation |