Human Motion Gesture Recognition Algorithm in Video Based on Convolutional Neural Features of Training Images
TL;DR: A new method of constructing human motion posture features to describe human behavior based on deep convolutional neural network features and topic models and a two-stage data division method for basketball is proposed.
read more
Abstract: The main work of human motion gesture recognition is to recognize and analyze the behavior of human objects in the video. Although the current research in the field of human motion gesture recognition has achieved certain results, the human motion gesture recognition in real life scenes has great effects due to factors such as camera movement, target scale transformation, dynamic background, viewing angle, and illumination. This article first proposes a new method of constructing human motion posture features to describe human behavior. This method is based on deep convolutional neural network features and topic models. Experiments have verified that compared with the traditional feature map extracted from the convolutional neural network fully connected layer, the feature map extracted from the convolutional neural network convolutional layer is not only lower in dimension but also has higher discrimination. Secondly, based on the feature map of the convolutional neural network, the training map downsampling strategy is used to overcome the interference caused by the object's scale change and shape change. Finally, based on the basketball gesture recognition method, the behavior performance of the legs and arms in 9 basketball actions of walking, running, jumping, standing dribbling, walking dribbling, running dribbling, shooting, passing and receiving is analyzed. As well as the corresponding signal waveform characteristics, a two-stage data division method for basketball is proposed. The unit action data is extracted for analysis to realize feature extraction. In order to select the most suitable classifier for basketball gesture recognition, the constructed feature vector uses four Different classifiers are trained to construct different classifiers to realize the division of actions.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Video Hand Gestures Recognition Using Depth Camera and Lightweight CNN
David González León,Jade Gröli,Sreenivasa Reddy Yeduri,Daniel Rossier,Romuald Mosqueron,Om Jee Pandey,Linga Reddy Cenkeramaddi +6 more
TL;DR: This work presents the video based hand gestures recognition using the depth camera and a light weight convolutional neural network (CNN) model, and compares the accuracy of the proposed light weight CNN model with the state-of-the hand gesture classification models.
47
Basketball action recognition based on FPGA and particle image
TL;DR: This method is proposed to integrate the FPGA into a network of two data streams in order to find the finest areas of basketball action recognition and extracts the features of the recognition system.
24
An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection.
TL;DR: This intelligent system can be an emerging technology based on wearable sensors and attain the experience-driven to data-driven transition in the field of sports training and talent selection and can be easily extended to analyze other foot-related sports motions and skill levels.
Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis.
TL;DR: In this paper, a human action recognition (HAR) algorithm has been designed in order to analyse the sports psychology of athletes and to identify the psychology of athlete in their movements. And the proposed optimised convolutional three-dimensional network (C3D) HAR model has a recognition accuracy of 80% with an image loss of 5.6.
Performance Evaluation of Support Vector Machine Algorithm for Human Gesture Recognition
TL;DR: This paper developed research methodology that is adapted PRISMA, consisted of four main steps for reviewing scientific articles, including identification, screening, eligibility and inclusion criteria, and conducted pilot study of SVM implementation for human gesture recognition.
11
References
A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs
TL;DR: Experimental results show that this first work based on deep CNNs for gait recognition in the literature outperforms the previous state-of-the-art methods by a significant margin, and shows great potential for practical applications.
765
Convolutional Neural Networks and Long Short-Term Memory for skeleton-based human activity and hand gesture recognition
TL;DR: A deep learning-based approach for temporal 3D pose recognition problems based on a combination of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) recurrent network and a data augmentation method that has also been validated experimentally is proposed.
413
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.
TL;DR: This work proposes an attention-based hybrid CNN and RNN (CNN-RNN) architecture to better capture temporal properties of electromyogram signal for gesture recognition problem and presents a new sEMG image representation method based on a traditional feature vector which enables deep learning architectures to extract implicit correlations between different channels for sparse multi-channel electromyograms.
An analysis of Convolutional Long Short-Term Memory Recurrent Neural Networks for gesture recognition
TL;DR: This research shows that CNNLSTM learns the temporal evolution of the gestures classifying correctly their meaningful part, known as Kendons stroke phase, and shows that the network learns to detect the most intense body motion.
257
Review of constraints on vision-based gesture recognition for human–computer interaction
TL;DR: Major constraints on vision-based gesture recognition occurring in detection and pre-processing, representation and feature extraction, and recognition are surveyed.