Pose-based human action recognition via sparse representation in dissimilarity space

Theodorakopoulos, Ilias; Kastaniotis, Dimitris; Economou, George; Fotopoulos, S.

doi:10.1016/j.jvcir.2013.03.008

Cited by 87 publications

(37 citation statements)

References 27 publications

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“…In this article, the proposed method is compared with the method of Xia et al [44] which has received considerable attention in literature [2], [13], [9], [46] and has been used for comparison in very recent methods [43], [3], [38], [18], [23]. Note that all the above methods has experimented with datasets created using an older version of Kinect sensor and not containing involuntary actions.…”

Section: Action Recognition and Comparisonmentioning

confidence: 99%

Recognizing Involuntary Actions from 3D Skeleton Data Using Body States

2018

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Abstract-Human action recognition has been one of the most active fields of research in computer vision over the last years. Two dimensional action recognition methods are facing serious challenges such as occlusion and missing the third dimension of data. Development of depth sensors has made it feasible to track positions of human body joints over time. This paper proposes a novel method for action recognition which uses temporal 3D skeletal Kinect data. This method introduces the definition of body states and then every action is modeled as a sequence of these states. The learning stage uses Fisher Linear Discriminant Analysis (LDA) to construct discriminant feature space for discriminating the body states. Moreover, this paper suggests the use of the Mahalonobis distance as an appropriate distance metric for the classification of the states of involuntary actions. Hidden Markov Model (HMM) is then used to model the temporal transition between the body states in each action. According to the results, this method significantly outperforms other popular methods, with recognition (recall) rate of 88.64% for eight different actions and up to 96.18% for classifying the class of all fall actions versus normal actions.Index Terms-Human action recognition, involuntary action recognition, Fisher, linear discriminant analysis (LDA), kinect, 3D skeleton data, hidden markov model (HMM).

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Section: Action Recognition and Comparisonmentioning

confidence: 99%

Recognizing Involuntary Actions from 3D Skeleton Data Using Body States

2018

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“…In Ref. [46], the coordinates of human skeleton models generate body poses and an action can be seen as a sequence of body poses over time. According to this approach, a feature vector is obtained representing each pose in a multidimensional feature space.…”

Section: Related Work On Rgb-d Sensorsmentioning

confidence: 99%

Human Action Recognition with RGB-D Sensors

Cippitelli¹,

Gambi²,

Spinsante³

2017

Motion Tracking and Gesture Recognition

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Human action recognition, also known as HAR, is at the foundation of many different applications related to behavioral analysis, surveillance, and safety, thus it has been a very active research area in the last years. The release of inexpensive RGB-D sensors fostered researchers working in this field because depth data simplify the processing of visual data that could be otherwise difficult using classic RGB devices. Furthermore, the availability of depth data allows to implement solutions that are unobtrusive and privacy preserving with respect to classic video-based analysis. In this scenario, the aim of this chapter is to review the most salient techniques for HAR based on depth signal processing, providing some details on a specific method based on temporal pyramid of key poses, evaluated on the well-known MSR Action3D dataset.

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“…In the related redundant atom library to get the constructive results highly the nonlinear approximation, we must first focus on the particular atoms on the library. For a group of signal, choose different components have different joint sparse effect together [26]. If can know to the collected signals, each node, so can be best to identify common components make joint sparse representation of sparse degree is minimal.…”

Section: The Sparse Coding Algorithmsmentioning

confidence: 99%