Skeleton-based action recognition with extreme learning machines

Chen, Xi; Koskela, Markus

doi:10.1016/j.neucom.2013.10.046

Cited by 43 publications

(31 citation statements)

References 26 publications

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“…A depth motion maps (DMM)-based human action recognition method using l2-regularized collaborative representation classifier is introduced. Third, method in [28] skeleton joint position information with temporal difference is produced as final feature, and extreme learning machine is used for action recognition. The comparison results are listed in Table 2.…”

Section: B Comparison With Other Methodsmentioning

confidence: 99%

“…By comparison, it can be seen that our scheme outperforms the approaches published in [13] in all three test cases. For the challenging cross subject test, algorithm in [28] produces better results on AS2 and AS3. The most probable reason for this may be that actions in the two subsets are more complicated and the proposed accurate joint position information can effectively solve the problems of high intra-class variability and inter-class similarity.…”

Section: B Comparison With Other Methodsmentioning

confidence: 99%

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Action Recognition using Key-Frame Features of Depth Sequence and ELM

Liu¹,

Wang²

2017

ijacsa

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Abstract-Recently, the rapid development of inexpensive RGB-D sensor, like Microsoft Kinect, provides adequate information for human action recognition. In this paper, a recognition algorithm is presented in which feature representation is generated by concatenating spatial features from human contour of key frames and temporal features from time difference information of a sequence. Then, an improved multi-hidden layers extreme learning machine is introduced as classifier. At last, we test our scheme on the public UTD-MHAD dataset from recognition accuracy and time consumption.

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Section: B Comparison With Other Methodsmentioning

confidence: 99%

Section: B Comparison With Other Methodsmentioning

confidence: 99%

Action Recognition using Key-Frame Features of Depth Sequence and ELM

Liu¹,

Wang²

2017

ijacsa

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“…In cases when hardware returns directly spatial coordinates of the body joints authors prefer to use feature that are derived from those coordinates. Among those features are various configurations of angle-based [6,10,46] and coordinate-based features [2,12]. Basing on our previous researches [26] the angle based-features gives better recognition results than coordinate -based.…”

Section: Application For Human Actions Recognitionmentioning

confidence: 99%

“…Our DTW classifier works on MoCap data recalculated according to (12). Each sample of recording is 12 -dimensional vector with angle values.…”

Section: Application For Human Actions Recognitionmentioning

confidence: 99%

Averaging of motion capture recordings for movements’ templates generation

Hachaj

Koptyra

Ogiela

2018

Multimed Tools Appl

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In this paper we propose, describe and evaluate the novel motion capture (MoCap) data averaging framework. It incorporates hierarchical kinematic model, angle coordinates' preprocessing methods, that recalculate the original MoCap recording making it applicable for further averaging algorithms, and finally signals averaging processing. We have tested two signal averaging methods namely Kalman Filter (KF) and Dynamic Time Warping barycenter averaging (DBA). The propose methods have been tested on MoCap recordings of elite Karate athlete, multiple champion of Oyama karate knockdown kumite who performed 28 different karate techniques repeated 10 times each. The proposed methods proved to have not only high effectiveness measured with root-mean-square deviation (4.04 ± 5.03 degrees for KF and 5.57 ± 6.27 for DBA) and normalized Dynamic Time Warping distance (0.90 ± 1.58 degrees for KF and 0.93 ± 1.23 for DBA), but also the reconstruction and visualization of those recordings persists all crucial aspects of those complicated actions. The proposed methodology has many important applications in classification, clustering, kinematic analysis and coaching. Our approach generates an averaged full body motion template that can be practically used for example for human actions Multimed Tools Appl (2018) 77:30353-30380 recognition. In order to prove it we have evaluated templates generated by our method in human action classification tasks using DTW classifier. We have made two experiments. In first leave -one -out cross -validation we have obtained 100% correct recognitions. In second experiment when we classified recordings of one person using templates of another recognition rate 94.2% was obtained.

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“…Kinect iskelet eklem yapısı (Kinect skeletal joint structure) [22] Eylem tanıma için 3B eklem koordinatları, iki adet ( ve ) 2B eklem koordinatlarına indirgenmesine dayalı bir model oluşturulmuştur [23,24] Seçilen ve eksenleri için 30°'lik açı ile oluşturulan model ve modeldeki bölgeler Şekil 2'de gösterilmiştir. Shotton yöntemine göre, eklem noktalarından kalça merkezi eklemi referans alınmıştır [25,26].…”

Section: İskelet Eklemlerinden öZnitelik çıKarılması (Feature Extractunclassified

Eklem tabanlı etkili düşme tespiti

Aslan

Akbulut²,

Şengür³

et al. 2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Günlük eylemlere ait derinlik ve iskelet veri setlerinin kinect ile oluşturulması  İskelet eklem özellik çıkarımı  3B iskelet verilerine dayalı yaşlı düşme tespiti Makale Bilgileri ÖZET Geliş: 08.03.2016 Kabul: 02.07.2017 DOI: Düşme yaşlılar için ölüm ve yaralanmalarda en önemli nedenlerden biridir. Gerçek zamanlı düşme tespiti yaşlıların güvenliği için büyük önem taşımaktadır. Bu çalışmada, düşme tespiti için iskelet eklem verilerine dayalı yeni bir yöntem önerilmiştir. 21 deneğin katılımı ile oluşturulan FUKinect-Fall veri setindeki üç boyutlu (3b) iskelet verileri önce iki adet (xy ve zy) iki boyut (2b) eklem verilerine indirgenmiştir. Daha sonra seçilen referans ekleme göre iç içe geçmiş daireler üzerine kodlanmış bölgeler oluşturularak her bir eksende kalan 19 eklemin bir eylem süresince bulunduğu bölge ortalamalarını içeren özellik matrisi çıkartılmıştır. Bu özellik matrisi k-En Yakın Komşu (k-EYK) ve Destek Vektör Makinası (DVM) ile sınıflandırılmıştır. Yapılan deneysel çalışmalarda %97,08 doğrulukta düşme tespiti yapılmıştır.

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Skeleton-based action recognition with extreme learning machines

Cited by 43 publications

References 26 publications

Action Recognition using Key-Frame Features of Depth Sequence and ELM

Action Recognition using Key-Frame Features of Depth Sequence and ELM

Averaging of motion capture recordings for movements’ templates generation

Eklem tabanlı etkili düşme tespiti

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