Search results for "Action classification"

showing 3 items of 3 documents

3D skeleton-based human action classification: A survey

2016

In recent years, there has been a proliferation of works on human action classification from depth sequences. These works generally present methods and/or feature representations for the classification of actions from sequences of 3D locations of human body joints and/or other sources of data, such as depth maps and RGB videos.This survey highlights motivations and challenges of this very recent research area by presenting technologies and approaches for 3D skeleton-based action classification. The work focuses on aspects such as data pre-processing, publicly available benchmarks and commonly used accuracy measurements. Furthermore, this survey introduces a categorization of the most recent…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniInformation retrievalBody pose representationPoint (typography)Computer science020207 software engineering02 engineering and technologySkeleton (category theory)computer.software_genreAction recognitionField (computer science)Action classificationAction (philosophy)CategorizationArtificial IntelligenceBody jointSignal Processing0202 electrical engineering electronic engineering information engineeringFeature (machine learning)020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionData miningcomputerSkeletonSoftwarePattern Recognition
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Hop: Histogram of patterns for human action representation

2017

This paper presents a novel method for representing actions in terms of multinomial distributions of frequent sequential patterns of different length. Frequent sequential patterns are series of data descriptors that occur many times in the data. This paper proposes to learn a codebook of frequent sequential patterns by means of an apriori-like algorithm, and to represent an action with a Bag-of-Frequent-Sequential-Patterns approach. Preliminary experiments of the proposed method have been conducted for action classification on skeletal data. The method achieves state-of-the-art accuracy value in cross-subject validation.

Apriori algorithmSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSeries (mathematics)Computer sciencebusiness.industryComputer Science (all)CodebookValue (computer science)Pattern recognition02 engineering and technologyAction classificationTheoretical Computer ScienceComputingMethodologies_PATTERNRECOGNITIONAction (philosophy)020204 information systemsHistogram0202 electrical engineering electronic engineering information engineeringFrequent pattern020201 artificial intelligence & image processingMultinomial distributionArtificial intelligenceHop (telecommunications)Representation (mathematics)business
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Gesture Modeling by Hanklet-Based Hidden Markov Model

2015

In this paper we propose a novel approach for gesture modeling. We aim at decomposing a gesture into sub-trajectories that are the output of a sequence of atomic linear time invariant (LTI) systems, and we use a Hidden Markov Model to model the transitions from the LTI system to another. For this purpose, we represent the human body motion in a temporal window as a set of body joint trajectories that we assume are the output of an LTI system. We describe the set of trajectories in a temporal window by the corresponding Hankel matrix (Hanklet), which embeds the observability matrix of the LTI system that produced it. We train a set of HMMs (one for each gesture class) with a discriminative a…

Conditional random fieldKinectbusiness.industryComputer scienceMaximum-entropy Markov modelAction ClassificationHankel matrixMarkov modelHidden Markov ModelLTI system theoryGestureAction RecognitionGesture recognitionObservabilityArtificial intelligencebusinessHidden Markov modelAlgorithmHankel matrixSkeleton
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