Supervised spatio-temporal kernel descriptor for human action recognition from RGB-depth videos

Asadi-Aghbolaghi, Maryam; Kasaei, Shohreh

doi:10.1007/s11042-017-5017-y

Cited by 13 publications

(7 citation statements)

References 46 publications

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“…If there is one of the K Gaussian distributions that satisfies the condition of formula (15), it is determined that the pixel is matched with it, and the mean, variance, and weight of the Gaussian distribution are updated. The update process is shown in the formula: The transition matrix of HMM 7 Wireless Communications and Mobile Computing…”

Section: Background Extraction Methodmentioning

confidence: 99%

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Footprint Extraction and Sports Dance Action Recognition Method Based on Artificial Intelligence Distributed Edge Computing

Hu¹,

2022

Wireless Communications and Mobile Computing

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Distributed edge computing technology for artificial intelligence refers to an emerging technology that integrates network core processing functions, computing functions, storage functions, etc. into one end source closer to objects or data on the basis of an open platform to optimize service quality. In this paper, distributed edge computing technology is applied to footprint extraction and sports dance action recognition, aimed at improving the recognition efficiency and recognition quality. Firstly, the overview of edge computing theory is introduced; these include edge computing concepts, edge computing characteristics, and edge computing platforms; and then, the classification of action recognition technology is described. Finally, the edge computing recognition technology and traditional recognition technology are compared and tested. The experimental results show that the average accuracy of edge computing technology for footprint extraction can reach 98.98, and the average recognition rate of sports dance movements can reach 80.21%, which verifies its practicability.

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Section: Background Extraction Methodmentioning

confidence: 99%

“…Therefore, dance movement recognition in reality usually relies on manual processing. The main part in the dance video sequence is manually segmented, cropped into multiple small videos, and then identified in sequence [15].…”

Section: Classification Of Action Recognition Technologymentioning

confidence: 99%

Footprint Extraction and Sports Dance Action Recognition Method Based on Artificial Intelligence Distributed Edge Computing

Hu¹,

2022

Wireless Communications and Mobile Computing

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“…Classification accuracy (%) DCSF [54] 89.3 HON4D [15] 88.9 Super Normal Vector [16] 93.1 Skeletons Lie group [17] 89.5 DMM-LBP-DF [55] 93.0 2D-CNN on DMM-Pyramid [44] 91.1 3D-CNN on DMM-Cube [44] 86.1 HOG3D + LLC [56] 90.9 Hierarchical 3D Kernel [57] 92.7 GLAC on DMM [13] 89.4 DMM-GLAC-STACOG [13] 94.8 3DHoT + MBC [58] 95.2 Subspace encoding [59] 94.06 LSTM + trust gates [60] 94.8 Extended SNV [61] 93.5 Trust Gates [62] 94.8 ST-NBNN [63] 94.8 SSTKDes [64] 95.6 3D-CNN + DHI + relief + SVM [65] 92.8 WDMM + HOG [66] 91.9 WDMM + LBP [66] 91.6 WDMM + CNN [66] 90.0 Deep activations [67] 92.3 Deep activations + attributes [67] 93.4 Hierarchical Gaussian [68] 95.6 GMHI + GSHI + CRC [69] 94.5 MHF + SHF + KELM [36] 95.97 Spatiotemporal + HMM [70] 92. Experimental evaluation of our approach on UTD-MHAD dataset is represented by Table 5.…”

Section: Approachmentioning

confidence: 99%

Gradient local auto-correlation features for depth human action recognition

Bulbul¹,

Ali

2021

SN Appl. Sci.

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Human action classification is a dynamic research topic in computer vision and has applications in video surveillance, human–computer interaction, and sign-language recognition. This paper aims to present an approach for the categorization of depth video oriented human action. In the approach, the enhanced motion and static history images are computed and a set of 2D auto-correlation gradient feature vectors is obtained from them to describe an action. Kernel-based Extreme Learning Machine is used with the extracted features to distinguish the diverse action types promisingly. The proposed approach is thoroughly assessed for the action datasets namely MSRAction3D, DHA, and UTD-MHAD. The approach achieves an accuracy of 97.44% for MSRAction3D, 99.13% for DHA, and 88.37% for UTD-MHAD. The experimental results and analysis demonstrate that the classification performance of the proposed method is considerable and surpasses the state-of-the-art human action classification methods. Besides, from the complexity analysis of the approach, it is turn out that our method is consistent for the real-time operation with low computational complexity.

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“…Some of these works are IJIUS 9,1 discussed in this section. The holistic feature, STIP is considered as a base feature for the dictionary (Killedar and Sasi, 2014;Roy et al, 2016;Asadi-Aghbolaghi and Kasaei, 2018;Nazir et al, 2018;Uddin and Lee, 2019). The sparse coding (sparse matrix construction) is performed using the dictionary and the input signal (STIP value of the video frames).…”

Section: Related Workmentioning

confidence: 99%

Skeleton-based STIP feature and discriminant sparse coding for human action recognition

Ushapreethi

2020

IJIUS

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PurposeTo find a successful human action recognition system (HAR) for the unmanned environments.Design/methodology/approachThis paper describes the key technology of an efficient HAR system. In this paper, the advancements for three key steps of the HAR system are presented to improve the accuracy of the existing HAR systems. The key steps are feature extraction, feature descriptor and action classification, which are implemented and analyzed. The usage of the implemented HAR system in the self-driving car is summarized. Finally, the results of the HAR system and other existing action recognition systems are compared.FindingsThis paper exhibits the proposed modification and improvements in the HAR system, namely the skeleton-based spatiotemporal interest points (STIP) feature and the improved discriminative sparse descriptor for the identified feature and the linear action classification.Research limitations/implicationsThe experiments are carried out on captured benchmark data sets and need to be analyzed in a real-time environment.Practical implicationsThe middleware support between the proposed HAR system and the self-driven car system provides several other challenging opportunities in research.Social implicationsThe authors’ work provides the way to go a step ahead in machine vision especially in self-driving cars.Originality/valueThe method for extracting the new feature and constructing an improved discriminative sparse feature descriptor has been introduced.

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Supervised spatio-temporal kernel descriptor for human action recognition from RGB-depth videos

Cited by 13 publications

References 46 publications

Footprint Extraction and Sports Dance Action Recognition Method Based on Artificial Intelligence Distributed Edge Computing

Footprint Extraction and Sports Dance Action Recognition Method Based on Artificial Intelligence Distributed Edge Computing

Gradient local auto-correlation features for depth human action recognition

Skeleton-based STIP feature and discriminant sparse coding for human action recognition

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