Research on Applied Technology in Human Action Recognition Based on Skeleton Information

2013 ◽  
Vol 859 ◽  
pp. 498-502 ◽  
Author(s):  
Zhi Qiang Wei ◽  
Ji An Wu ◽  
Xi Wang
Keyword(s):  
Data Analysis ◽  
Action Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Experimental Results ◽  
Human Actions ◽  
Applied Technology ◽  
Feature Sequence ◽  
Human Actions Recognition

In order to realize the identification of human daily actions, a method of identifying human daily actions is realized in this paper, which transforms this problem into converting human action recognition into analyzing feature sequence. Then the feature sequence combined with improved LCS algorithm could realize the human actions recognition. Data analysis and experimental results show the recognition rate of this method is high and speed is fast, and this applied technology will have broad prospects.

Human Action Recognition Based on Improved MIL

2015 ◽  
Vol 713-715 ◽  
pp. 2152-2155 ◽  
Author(s):  
Shao Ping Zhu
Keyword(s):  
Computer Vision ◽  
Action Recognition ◽  
Heuristic Algorithm ◽  
Human Action Recognition ◽  
Human Action ◽  
Image Sequences ◽  
Multiple Instance Learning ◽  
New Method ◽  
Human Actions ◽  
Human Actions Recognition

According to the problem that achieves robust human actions recognition from image sequences in computer vision, using the Iterative Querying Heuristic algorithm as a guide, a improved Multiple Instance Learning (MIL) method is proposed for human action recognition in video image sequences. Experiments show that the new method can quickly recognize human actions and achieve high recognition rates, and on the Weizmann database validate our analysis.

An Adaptive Stacked Denoising Auto-Encoder Architecture for Human Action Recognition

2014 ◽  
Vol 631-632 ◽  
pp. 403-409 ◽  
Author(s):  
Dao Xi Wu ◽  
Wei Pan ◽  
Li Dong Xie ◽  
Chao Xi Huang
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Adaptive Learning ◽  
Human Action Recognition ◽  
Human Action ◽  
Learning Rate ◽  
Experimental Results ◽  
Classification Methods ◽  
Initial Value ◽  
Human Actions

In this paper, a stacked denoising auto-encoder architecture method with adaptive learning rate for action recognition based on skeleton features of human is presented. Firstly a Kinect is used for capturing the skeleton images and extracting skeleton features. Then an adaptive stacked denoising auto-encoder with three hidden layers is constructed for unsupervised pre-training. So the trained weights are achieved. Finally, a neural network is constructed for action recognition, in which the trained weights are used as the initial value, covering the random value. Based on the experimental results from the Kinect dataset of human actions sampled in experiments, it is clear to see that our method possesses the better robustness and accuracy, compared with the classic classification methods.

scholarly journals A novel feature fusion based Human Action Recognition in 2D Videos

2018 ◽  
Vol 7 (2.20) ◽  
pp. 207 ◽  
Author(s):  
K Rajendra Prasad ◽  
P Srinivasa Rao
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Scale Invariant ◽  
Human Actions

Human action recognition from 2D videos is a demanding area due to its broad applications. Many methods have been proposed by the researchers for recognizing human actions. The improved accuracy in identifying human actions is desirable. This paper presents an improved method of human action recognition using support vector machine (SVM) classifier. This paper proposes a novel feature descriptor constructed by fusing the various investigated features. The handcrafted features such as scale invariant feature transform (SIFT) features, speed up robust features (SURF), histogram of oriented gradient (HOG) features and local binary pattern (LBP) features are obtained on online 2D action videos. The proposed method is tested on different action datasets having both static and dynamically varying backgrounds. The proposed method achieves shows best recognition rates on both static and dynamically varying backgrounds. The datasets considered for the experimentation are KTH, Weizmann, UCF101, UCF sports actions, MSR action and HMDB51.The performance of the proposed feature fusion model with SVM classifier is compared with the individual features with SVM. The fusion method showed best results. The efficiency of the classifier is also tested by comparing with the other state of the art classifiers such as k-nearest neighbors (KNN), artificial neural network (ANN) and Adaboost classifier. The method achieved an average of 94.41% recognition rate.  

Human action recognition using simple geometric features and a finite state machine

2013 ◽  
Vol 18 (2-3) ◽  
pp. 49-60 ◽  
Author(s):  
Damian Dudzńiski ◽  
Tomasz Kryjak ◽  
Zbigniew Mikrut
Keyword(s):  
Action Recognition ◽  
Finite State Machine ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Stream ◽  
State Machine ◽  
Recognition Algorithm ◽  
Finite State ◽  
Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

Research on Action Recognition of Human Body Based on Kinect Sensor

2014 ◽  
Vol 644-650 ◽  
pp. 4162-4166
Author(s):  
Dan Dan Guo ◽  
Xi’an Zhu
Keyword(s):  
Action Recognition ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Motion ◽  
Skeletal Structure ◽  
Depth Sensor ◽  
Depth Sensors ◽  
3D Space ◽  
3D Information

An effective Human action recognition method based on the human skeletal information which is extracted by Kinect depth sensor is proposed in this paper. Skeleton’s 3D space coordinates and the angles between nodes of human related actions are collected as action characteristics through the research of human skeletal structure, node data and research on human actions. First, 3D information of human skeletons is acquired by Kinect depth sensors and the cosine of relevant nodes is calculated. Then human skeletal information within the time prior to current state is stored in real time. Finally, the relevant locations of the skeleton nodes and the variation of the cosine of skeletal joints within a certain time are analyzed to recognize the human motion. This algorithm has higher adaptability and practicability because of the complicated sample trainings and recognizing processes of traditional method is not taken up. The results of the experiment indicate that this method is with high recognition rate.

VISUAL-BASED HUMAN ACTION RECOGNITION ON SMART PHONES BASED ON 2D AND 3D DESCRIPTORS

2012 ◽  
Vol 26 (08) ◽  
pp. 1260009 ◽  
Author(s):  
MARC BOSCH-JORGE ◽  
ANTONIO-JOSÉ SÁNCHEZ-SALMERÓN ◽  
CARLOS RICOLFE-VIALA
Keyword(s):  
Action Recognition ◽  
Human Subjects ◽  
Recognition Rate ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Smart Phones ◽  
Image Representations ◽  
And Performance ◽  
3D Descriptors

The aim of this work is to present a visual-based human action recognition system which is adapted to constrained embedded devices, such as smart phones. Basically, vision-based human action recognition is a combination of feature-tracking, descriptor-extraction and subsequent classification of image representations, with a color-based identification tool to distinguish between multiple human subjects. Simple descriptors sets were evaluated to optimize recognition rate and performance and two dimensional (2D) descriptors were found to be effective. These sets installed on the latest phones can recognize human actions in videos in less than one second with a success rate of over 82%.

Multi-Channel MKL for Video Human Action Recognition

2014 ◽  
Vol 599-601 ◽  
pp. 1571-1574
Author(s):  
Jia Ding ◽  
Yang Yi ◽  
Ze Min Qiu ◽  
Jun Shi Liu
Keyword(s):  
Action Recognition ◽  
Recognition Rate ◽  
Multiple Kernel Learning ◽  
Human Action Recognition ◽  
Human Action ◽  
Image Understanding ◽  
Kernel Learning ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Multiple Kernel

Human action recognition in videos plays an important role in the field of computer vision and image understanding. A novel method of multi-channel bag of visual words and multiple kernel learning is proposed in this paper. The videos are described by multi-channel bag of visual words, and a multiple kernel learning classifier is used for action classification, in which each kernel function of the classifier corresponds to a video channel in order to avoid the noise interference from other channels. The proposed approach improves the ability in distinguishing easily confused actions. Experiments on KTH show that the presented method achieves remarkable performance on the average recognition rate, and obtains comparable recognition rate with state-of-the-art methods.

scholarly journals Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiulin Wang ◽  
Baole Tao ◽  
Fulei Han ◽  
Wenting Wei
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Accuracy ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Basketball Players ◽  
Human Actions ◽  
Basketball Game ◽  
Wide Range

The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.

Recognition of Inter-Class Variation of Human Actions in Sports Video

2021 ◽  
Author(s):  
Akila.K
Keyword(s):  
Action Recognition ◽  
Wavelet Packet ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Human Actions ◽  
Linear Discriminant ◽  
Novel Approach ◽  
Current Events ◽  
Rule Set

Abstract Background: Human action recognition encompasses a scope for an automatic analysis of current events from video and has varied applications in multi-various fields. Recognizing and understanding of human actions from videos still remains a difficult downside as a result of the massive variations in human look, posture and body size inside identical category.Objective: This paper focuses on a specific issue related to inter-class variation in Human Action Recognition.Approach: To discriminate the human actions among the category, a novel approach which is based on wavelet packet transformation for feature extraction. As we are concentrating on classifying similar actions non-linearity among the features are analyzed and discriminated by Deterministic Normalized - Linear Discriminant Analysis (DN-LDA). However the major part of the recognition system relays on classification part and the dynamic feeds are classified by Hidden Markov Model at the final stage based on rule set..Conclusion: Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the inter-class variation

scholarly journals Drone-Action: An Outdoor Recorded Drone Video Dataset for Action Recognition

Drones ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 82 ◽  
Author(s):  
Asanka G. Perera ◽  
Yee Wei Law ◽  
Javaan Chahl
Keyword(s):  
Action Recognition ◽  
Situational Awareness ◽  
Human Action Recognition ◽  
Human Action ◽  
Machine Learning Techniques ◽  
High Definition ◽  
Human Actions ◽  
Research Areas ◽  
Indoor Scenes ◽  
High Level

Aerial human action recognition is an emerging topic in drone applications. Commercial drone platforms capable of detecting basic human actions such as hand gestures have been developed. However, a limited number of aerial video datasets are available to support increased research into aerial human action analysis. Most of the datasets are confined to indoor scenes or object tracking and many outdoor datasets do not have sufficient human body details to apply state-of-the-art machine learning techniques. To fill this gap and enable research in wider application areas, we present an action recognition dataset recorded in an outdoor setting. A free flying drone was used to record 13 dynamic human actions. The dataset contains 240 high-definition video clips consisting of 66,919 frames. All of the videos were recorded from low-altitude and at low speed to capture the maximum human pose details with relatively high resolution. This dataset should be useful to many research areas, including action recognition, surveillance, situational awareness, and gait analysis. To test the dataset, we evaluated the dataset with a pose-based convolutional neural network (P-CNN) and high-level pose feature (HLPF) descriptors. The overall baseline action recognition accuracy calculated using P-CNN was 75.92%.

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