scholarly journals The Approach for Action Recognition Based on the Reconstructed Phase Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hong-bin Tu ◽  
Li-min Xia
Keyword(s):  
Phase Space ◽  
Particle Filter ◽  
Action Recognition ◽  
Human Body ◽  
Probability Model ◽  
Human Action Recognition ◽  
Human Action ◽  
Key Points ◽  
Reconstructed Phase Space ◽  
Novel Method

This paper presents a novel method of human action recognition, which is based on the reconstructed phase space. Firstly, the human body is divided into 15 key points, whose trajectory represents the human body behavior, and the modified particle filter is used to track these key points for self-occlusion. Secondly, we reconstruct the phase spaces for extracting more useful information from human action trajectories. Finally, we apply the semisupervised probability model and Bayes classified method for classification. Experiments are performed on the Weizmann, KTH, UCF sports, and our action dataset to test and evaluate the proposed method. The compare experiment results showed that the proposed method can achieve was more effective than compare methods.

scholarly journals Study on Machine Learning and Deep Learning Methods for Human Action Recognition

2020 ◽  
Author(s):  
Gopika Rajendran ◽  
Ojus Thomas Lee ◽  
Arya Gopi ◽  
Jais jose ◽  
Neha Gautham
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Action Recognition ◽  
Human Body ◽  
Human Performance ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Robot Interaction ◽  
Learning Approach ◽  
Action Sequence

With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

Action Recognition Based on Motion Representing and Reconstructed Phase Spaces Matching of 3D Joint Positions

2013 ◽  
Vol 333-335 ◽  
pp. 675-679
Author(s):  
Yan Tao Zhao ◽  
Bo Zhang ◽  
Xu Guang Zhang ◽  
Xiao Li Li ◽  
Mei Ling Fu ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Nonlinear Dynamical Systems ◽  
Human Action Recognition ◽  
Human Action ◽  
Polar Space ◽  
Phase Portraits ◽  
Nonlinear Dynamical ◽  
Human Pose ◽  
Body Joints

This paper presents an efficient and novel framework for human action recognition based on representing the motion of human body-joints and the theory of nonlinear dynamical systems. Our work is motivated by the pictorial structures model and advances in human pose estimation. Intuitively, a collective understanding of human joints movements can lead to a better representation and understanding of any human action through quantization in the polar space. We use time-delay embedding on the time series resulting of the evolution of human body-joints variables along time to reconstruct phase portraits. Moreover, we train SVM models for action recognition by comparing the distances between trajectories of human body-joints variables within the reconstructed phase portraits. The proposed framework is evaluated on MSR-Action3D dataset and results compared against several state-of-the-art methods.

scholarly journals Local Random Sparse Coding for Human Action Recognition in Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Zhong Zhang ◽  
Shuang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Random Projection ◽  
Human Action ◽  
Wireless Sensor ◽  
Real World Applications ◽  
Novel Method

Recognizing human action in wireless sensor networks (WSN) has raised a great interest owing to the requirements of real-world applications. Recently, the bag-of-features model (BOF) has proved effective in human action recognition. In this paper, we propose a novel method named local random sparse coding (LRSC) for human action recognition in WSN based on the BOF model. The contribution is twofold. First, we utilize random projection (RP) technique for each feature vector to alleviate the curse of dimensionality. Second, we consider the locality of codebook and correspondingly propose to reconstruct the features using similar codewords. Our method is verified on the KTH and UCF Sports databases, and the experimental results demonstrate that our method achieves better results than that of previous methods on human action recognition in WSN.

scholarly journals Human action recognition using descriptor based on selective finite element analysis

2019 ◽  
Vol 70 (6) ◽  
pp. 443-453 ◽  
Author(s):  
Rajiv Kapoor ◽  
Om Mishra ◽  
Madan Mohan Tripathi
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Action Recognition ◽  
Human Body ◽  
Stiffness Matrix ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Frame ◽  
Element Analysis ◽  
Local Descriptor

Abstract This paper proposes a novel local descriptor evaluated from the Finite Element Analysis for human action recognition. This local descriptor represents the distinctive human poses in the form of the stiffness matrix. This stiffness matrix gives the information of motion as well as shape change of the human body while performing an action. Initially, the human body is represented in the silhouette form. Most prominent points of the silhouette are then selected. This silhouette is discretized into several finite small triangle faces (elements) where the prominent points of the boundaries are the vertices of the triangles. The stiffness matrix of each triangle is then calculated. The feature vector representing the action video frame is constructed by combining all stiffness matrices of all possible triangles. These feature vectors are given to the Radial Basis Function-Support Vector Machine (RBF-SVM) classifier. The proposed method shows its superiority over other existing state-of-the-art methods on the challenging datasets Weizmann, KTH, Ballet, and IXMAS.

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.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action
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