Landmark-based multimodal human action recognition

Asteriadis, Stylianos; Daras, Petros

doi:10.1007/s11042-016-3945-6

Cited by 14 publications

(5 citation statements)

References 31 publications

(41 reference statements)

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“…To enhance robustness to outliers, the Cauchy estimator is imposed on the reconstruction error. Asteriadis et al [29] presented a novel, multimodal human action recognition method to handle a sensing device's noise and person-specific characteristics. Each action is represented by a basis vector and spectral analysis is performed on an affinity matrix of new action feature vectors.…”

Section: Multimodal Feature Fusion-based Methodmentioning

confidence: 99%

Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

Tang

Wang

et al. 2020

Mathematical Problems in Engineering

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The representation and selection of action features directly affect the recognition effect of human action recognition methods. Single feature is often affected by human appearance, environment, camera settings, and other factors. Aiming at the problem that the existing multimodal feature fusion methods cannot effectively measure the contribution of different features, this paper proposed a human action recognition method based on RGB-D image features, which makes full use of the multimodal information provided by RGB-D sensors to extract effective human action features. In this paper, three kinds of human action features with different modal information are proposed: RGB-HOG feature based on RGB image information, which has good geometric scale invariance; D-STIP feature based on depth image, which maintains the dynamic characteristics of human motion and has local invariance; and S-JRPF feature-based skeleton information, which has good ability to describe motion space structure. At the same time, multiple K-nearest neighbor classifiers with better generalization ability are used to integrate decision-making classification. The experimental results show that the algorithm achieves ideal recognition results on the public G3D and CAD60 datasets.

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Section: Multimodal Feature Fusion-based Methodmentioning

confidence: 99%

Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

Tang

Wang

et al. 2020

Mathematical Problems in Engineering

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“…In Table A.5 ("Appendix 1"), the description of sensor-datasets is illustrated with attributes such as data source, #factors, sensor location, and activity type. It includes wearable sensor-based datasets (Alsheikh et al 2016;Asteriadis and Daras 2017;Zhang et al 2012;Chavarriaga et al 2013;Munoz-Organero 2019;Roggen et al 2010;Qin et al 2019), as well as smart-device sensor-based datasets (Ravi et al 2016;Cui and Xu 2013;Weiss et al 2019;Miu et al 2015;Reiss and Stricker 2012a, b;Lv et al 2020;Gani et al 2019;Stisen et al 2015;Röcker et al 2017;Micucci et al 2017) Apart from datasets mentioned in Table A.5, there are few more datasets worth mentioning such as Kasteren dataset (Kasteren et al 2011;, which is also very popular. (2) Vision-based HAR: Devices for collecting 3D data are CCTV cameras (Koppula and Saxena 2016;Devanne et al 2015;Zhang and Parker 2016;Li et al 2010;Duan et al 2020;Kalfaoglu et al 2020;Gorelick et al 2007;Mahadevan et al 2010), depth cameras (Cippitelli et al 2016;Gaglio et al 2015;Neili Boualia and Essoukri Ben Amara 2021;Ding et al 2016; Cornell Activity Datasets: CAD-60 & CAD-120 2021), and videos from public domains like YouTube and Hollywood movie scenes (Gu et al 2018;Soomro et al 2012;Kuehne et al 2011;Sigurdsson et...…”

Section: A Short Note On Har Datasetsmentioning

confidence: 99%

Human activity recognition in artificial intelligence framework: a narrative review

et al. 2022

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Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data. While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications; (2) HAR has dominated the healthcare industry; (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias; (4) little work was observed in abnormality detection during actions; and (5) almost no work has been done in forecasting actions. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. (b) AI will provide a powerful impetus to the HAR industry in future. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-021-10116-x.

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“…In this section, the related work about people counting and activity recognition is introduced in brief. The traditional method of human sensing is based on video [5][6][7]. It uses the camera to obtain scene information to realize the people counting and the recognition of actions.…”

Section: Related Workmentioning

confidence: 99%

Area human sensing via ambient Wi‐Fi signals

Chen

2021

IET Communications

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Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi-Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multidimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two-level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one-person and two-person is 99.6% and 94.6% respectively.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Landmark-based multimodal human action recognition

Cited by 14 publications

References 31 publications

Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

Using a Multilearner to Fuse Multimodal Features for Human Action Recognition

Human activity recognition in artificial intelligence framework: a narrative review

Area human sensing via ambient Wi‐Fi signals

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