Hierarchical Human Action Recognition by Normalized-Polar Histogram

Ziaeefard, Maryam; Ebrahimnezhad, Hossein

doi:10.1109/icpr.2010.906

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Ziaeefard and Ebrahimnezhad [94] proposed the cumulative skeletonized image (CSI) across time as features, and constructed 2-D angular/distance histograms based on it. A hierarchical SVM was used for the matching process.…”

Section: Action Recognition With Space-time Volumesmentioning

confidence: 99%

Advances in Human Action Recognition: A Survey

Cheng,

Wan,

Saudagar

et al. 2015

Preprint

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Human action recognition has been an important topic in computer vision due to its many applications such as video surveillance, human machine interaction and video retrieval. One core problem behind these applications is automatically recognizing low-level actions and high-level activities of interest. The former is usually the basis for the latter. This survey gives an overview of the most recent advances in human action recognition during the past several years, following a well-formed taxonomy proposed by a previous survey [1]. From this state-of-the-art survey, researchers can view a panorama of progress in this area for future research.

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Section: Action Recognition With Space-time Volumesmentioning

confidence: 99%

Advances in Human Action Recognition: A Survey

Cheng,

Wan,

Saudagar

et al. 2015

Preprint

View full text Add to dashboard Cite

show abstract

“…Ziaeefard and Ebrahimnezhad [23] proposed the cumulative skeletonized image (CSI) crosswise over time as features, and built 2D rakish/separation histograms in light of it. A hierarchical SVM was utilized for the coordinating procedure.…”

Section: Action Recognition With Space-time Volumesmentioning

confidence: 99%

A Review on Machine Learning Algorithms on Human Action Recognition

Rai

2017

Asian J Pharm Clin Res

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Human action recognition is a vital field of computer vision research. Its applications incorporate observation frameworks, patient monitoring frameworks, and an assortment of frameworks that include interactions between persons and electronic gadgets, for example, human-computer interfaces. The vast majority of these applications require an automated recognition of abnormal or anomalistic action states, made out of various straightforward (or nuclear) actions of persons. This study gives an overview of different best in class research papers on human movement recognition. Open datasets intended for the assessment of the recognition procedures are also discussed in this paper too, for comparing results of several methodologies on this datasets. We examine both the approaches produced for basic human actions and those for abnormal action states. These methodologies are taxonomically classified based on looking at the points of interest and constraints of every methodology. Space-time volume approaches and sequential methodologies that represent actions and perceive such action sets straightforwardly from images are discussed. Next, hierarchical recognition approaches for abnormal action states are introduced and looked at. Statistic-based methodologies, syntactic methodologies, and description-based methodologies for hierarchical recognition are examined in the paper.

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“…Methods that explicitly model relative changes in spatial descriptors over time [1], or estimates of global and local motion [10,14] have also been used. Methods that use the silhouette of the body to construct more sophisticated representation for human action have been proposed [36]. More recently, spatio-temporal feature-based approaches have been proposed and demonstrated for various action recognition applications.…”

Section: Introductionmentioning

confidence: 99%

Modeling Motion of Body Parts for Action Recognition

Tran

Kakadiaris

Shah

2011

Procedings of the British Machine Vision Conference 2011

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This paper presents a simple and computationally efficient framework for human action recognition based on modeling the motion of human body parts. Intuitively, a collective understanding of human part movements can lead to better understanding and representation of any human action. In this paper, we propose a generative representation of the motion of the human body parts to learn and classify human actions. The proposed representation combines the advantages of both local and global representations, encoding the relevant motion information as well as being robust to local appearance changes. Our work is motivated by the pictorial structures model and the framework of sparse representations for recognition. Human part movements are represented efficiently through quantization in the polar space. The key discrimination within each action is efficiently encoded by sparse representation to perform classification. The proposed method is evaluated on both the KTH and the UCF action datasets and the results are compared against other state-of-the-art methods.

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Hierarchical Human Action Recognition by Normalized-Polar Histogram

Cited by 12 publications

References 10 publications

Advances in Human Action Recognition: A Survey

Advances in Human Action Recognition: A Survey

A Review on Machine Learning Algorithms on Human Action Recognition

Modeling Motion of Body Parts for Action Recognition

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