Human Action Recognition Using a New Hybrid Descriptor

Mishra, Om; Kavimandan, P. Sh.; Tripathi, M. M.; Kapoor, Rajiv; Yadav, Kalpana

doi:10.1007/978-981-15-6840-4_43

Cited by 5 publications

(9 citation statements)

References 27 publications

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“…The main disadvantage of these methodologies is that they give only motion information but no information about the structure. Methodologies reported in literature [15][16][17][18][19][20][21][22] based on silhouette analysis have a major contribution to human action recognition. Kapoor et al [15] have used average energy silhouette images whereas a hybrid classifier is used for action recognition by Mishra et al [16].…”

Section: Introductionmentioning

confidence: 99%

“…Methodologies reported in literature [15][16][17][18][19][20][21][22] based on silhouette analysis have a major contribution to human action recognition. Kapoor et al [15] have used average energy silhouette images whereas a hybrid classifier is used for action recognition by Mishra et al [16]. Wu and Shao [17] used hybrid features which include both global and local features.…”

Section: Introductionmentioning

confidence: 99%

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Modal Frequencies Based Human Action Recognition Using Silhouettes And Simplicial Elements

Kavimandan

Mishra

Kapoor

2022

IJE

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Human action recognition has been a pioneer research problem among researchers. This paper proposed a new local feature descriptor in terms of modal frequency using silhouette and simplicial elements of a silhouette with the help of Finite Element Analysis (FEA). This local descriptor represents the distinctive human poses in the form of modal frequency. These modal frequencies reduce the feature dimension and represent a wide range of poses of human action. These modal frequencies are subject to the stiffness matrix of the body that is associated with the displacement. The silhouettes of the human body are used for the analysis. These silhouettes are represented into simplicial elements. The modal frequencies of silhouettes are calculated using simplicial elements. These modal frequencies of the silhouette are used as the feature vectors that are given to the Radial Basis Function-Support Vector Machine (RBF-SVM) classifier. The challenging datasets Weizmann, KTH and IXMAS are used for validation of the proposed methodology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modal Frequencies Based Human Action Recognition Using Silhouettes And Simplicial Elements

Kavimandan

Mishra

Kapoor

2022

IJE

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“…Human action recognition (HAR) emerged as an active research area in the field of computer vision (CV) in the last decade [ 1 ]. HAR has applications in various domains including; surveillance [ 2 ], human-computer interaction (HCI) [ 3 ], video reclamation, and understanding of visual information [ 4 ], etc. The most important application of action recognition is video surveillance [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Khan

Alhaisoni

et al. 2021

Sensors

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Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision (CV) have been proposed in the literature which did not prove to be successful due to large video sequences which need to be processed in surveillance systems. The problem exacerbates in the presence of multi-view cameras. Recently, the development of deep learning (DL)-based systems has shown significant success for HAR even for multi-view camera systems. In this research work, a DL-based design is proposed for HAR. The proposed design consists of multiple steps including feature mapping, feature fusion and feature selection. For the initial feature mapping step, two pre-trained models are considered, such as DenseNet201 and InceptionV3. Later, the extracted deep features are fused using the Serial based Extended (SbE) approach. Later on, the best features are selected using Kurtosis-controlled Weighted KNN. The selected features are classified using several supervised learning algorithms. To show the efficacy of the proposed design, we used several datasets, such as KTH, IXMAS, WVU, and Hollywood. Experimental results showed that the proposed design achieved accuracies of 99.3%, 97.4%, 99.8%, and 99.9%, respectively, on these datasets. Furthermore, the feature selection step performed better in terms of computational time compared with the state-of-the-art.

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“…In severe cases, these movements could damage physical functions [1][2][3][4]. Standard sports demonstrative movements need to meet the quality standards in terms of direction, sequence, rhythm, trajectory, space, and time [5][6][7][8][9]. To improve PE teaching and practice, it is important to analyze the technical features of each link in the specific sports movement.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Wrong Sports Movements Based on Deep Neural Network

Wang¹

2020

RIA

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During physical education (PE), the teaching quality is severely affected by problems like nonstandard technical movements or wrong demonstrative movements. High-speed photography can capture instantaneous movements that cannot be recognized with naked eyes. Therefore, this technology has been widely used to judge the sprint movements in track and field competitions, and assess the quality of artistic gymnastics. Inspired by three-dimensional (3D) image analysis, this paper proposes a method to recognize the standard and wrong demonstrative sports movements, based on 3D convolutional neural network (CNN) and graph theory. Firstly, a 3D posture perception strategy for demonstrative sports movements was constructed based on video sequence. Next, the authors provided the framework of the recognition system for standard and wrong demonstrative sports movements. After that, a 3D CNN was stablished to distinguish between standard and wrong demonstrative sports movements. The proposed method was proved effective and superior through experiments. The research results provide a good reference for the application of 3D image analysis in the recognition of other body behaviors and movements.

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Human Action Recognition Using a New Hybrid Descriptor

Cited by 5 publications

References 27 publications

Modal Frequencies Based Human Action Recognition Using Silhouettes And Simplicial Elements

Modal Frequencies Based Human Action Recognition Using Silhouettes And Simplicial Elements

Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Recognition of Wrong Sports Movements Based on Deep Neural Network

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