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

Image Based Human Action Recognition System to Support the Blind

2015 ◽  
Vol 42 (1) ◽  
pp. 138-143
Author(s):  
ByoungChul Ko ◽  
Mincheol Hwang ◽  
Jae-Yeal Nam
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action

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%.

HUMAN ACTION RECOGNITION USING META-COGNITIVE NEURO-FUZZY INFERENCE SYSTEM

2012 ◽  
Vol 22 (06) ◽  
pp. 1250028 ◽  
Author(s):  
K. SUBRAMANIAN ◽  
S. SURESH
Keyword(s):  
Action Recognition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Sequential Learning ◽  
Inference System ◽  
Neuro Fuzzy

We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.

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.

scholarly journals Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

2017 ◽  
Vol 7 (1.1) ◽  
pp. 489 ◽  
Author(s):  
P V.V. Kishore ◽  
P Siva Kameswari ◽  
K Niharika ◽  
M Tanuja ◽  
M Bindu ◽  
...  
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Graph Kernels ◽  
Joint Distributions ◽  
Spatial Graph ◽  
3D Space ◽  
Machine Interface ◽  
Action Frame

Human action recognition is a vibrant area of research with multiple application areas in human machine interface. In this work, we propose a human action recognition based on spatial graph kernels on 3D skeletal data. Spatial joint features are extracted using joint distances between human joint distributions in 3D space. A spatial graph is constructed using 3D points as vertices and the computed joint distances as edges for each action frame in the video sequence. Spatial graph kernels between the training set and testing set are constructed to extract similarity between the two action sets. Two spatial graph kernels are constructed with vertex and edge data represented by joint positions and joint distances. To test the proposed method, we use 4 publicly available 3D skeletal datasets from G3D, MSR Action 3D, UT Kinect and NTU RGB+D. The proposed spatial graph kernels result in better classification accuracies compared to the state of the art models.

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