Ongoing human action recognition with motion capture

Barnachon, Mathieu; Bouakaz, Saïda; Boufama, Boubakeur; Guillou, Erwan

doi:10.1016/j.patcog.2013.06.020

Cited by 120 publications

(56 citation statements)

References 29 publications

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“…The method compares the state of the art methods such as dynamic time wrapping (DTW) [16], weighted graph matching (WGM) [17], adaptive graph kernels [13], histogram [18] and locally preserving positions bag of words(LPP-BOW) [24]. We test the performance of our proposed spatial graph kernels (SGK) algorithm for validating the results with respect to precision-recall and percentage of recognition.…”

Section: Resultsmentioning

confidence: 99%

“…Features such as 3D graph joint trajectory locations [12] and joint relative distances [13] are used for human motion analysis. The features form human actions are classified using support vector machine [14], convolutional Neural Networks [15], Dynamic Time Warping [16], weighted graph matching [17] and Histogram [18]. However, JRD's and RRJRD's based descriptors for human action recognition were successfully used with graph kernel matching in [13] [14].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

Kishore¹,

Kameswari²,

Niharika³

et al. 2017

IJET

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Human action recognition is a vibrant area of research with multiple application areas in human machine interface. In this work, we propose a human action recognition based on spatial graph kernels on 3D skeletal data. Spatial joint features are extracted using joint distances between human joint distributions in 3D space. A spatial graph is constructed using 3D points as vertices and the computed joint distances as edges for each action frame in the video sequence. Spatial graph kernels between the training set and testing set are constructed to extract similarity between the two action sets. Two spatial graph kernels are constructed with vertex and edge data represented by joint positions and joint distances. To test the proposed method, we use 4 publicly available 3D skeletal datasets from G3D, MSR Action 3D, UT Kinect and NTU RGB+D. The proposed spatial graph kernels result in better classification accuracies compared to the state of the art models.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

Kishore¹,

Kameswari²,

Niharika³

et al. 2017

IJET

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show abstract

“…4. Specifically, given K human joints with [175] Vector of Joints Conc Lowlv Hand Patsadu et al [176] Vector of Joints Conc Lowlv Hand Huang and Kitani [177] Cost Topology Stat Lowlv Hand Devanne et al [178] Motion Units Conc Manif Hand Wang et al [179] Motion Poselets BoW Body Dict Wei et al [180] Structural Prediction Conc Lowlv Hand Gupta et al [181] 3D Pose w/o Body Parts Conc Lowlv Hand Amor et al [182] Skeleton's Shape Conc Manif Hand Sheikh et al [183] Action Space Conc Lowlv Hand Yilma and Shah [184] Multiview Geometry Conc Lowlv Hand Gong et al [185] Structured Time Conc Manif Hand Rahmani and Mian [186] Knowledge Transfer BoW Lowlv Dict Munsell et al [187] Motion Biometrics Stat Lowlv Hand Lillo et al [188] Composable Activities BoW Lowlv Dict Wu et al [189] Watch-n-Patch BoW Lowlv Dict Gong and Medioni [190] Dynamic Manifolds BoW Manif Dict Han et al [191] Hierarchical Manifolds BoW Manif Dict Slama et al [192,193] Grassmann Manifolds BoW Manif Dict Devanne et al [194] Riemannian Manifolds Conc Manif Hand Huang et al [195] Shape Tracking Conc Lowlv Hand Devanne et al [196] Riemannian Manifolds Conc Manif Hand Zhu et al [197] RNN with LSTM Conc Lowlv Deep Chen et al [198] EnwMi Learning BoW Lowlv Dict Hussein et al [199] Covariance of 3D Joints Stat Lowlv Hand Shahroudy et al [200] MMMP BoW Body Unsup Jung and Hong [201] Elementary Moving Pose BoW Lowlv Dict Evangelidis et al [202] Skeletal Quad Conc Lowlv Hand Azary and Savakis [203] Grassmann Manifolds Conc Manif Hand Barnachon et al [204] Hist. of Action Poses Stat Lowlv Hand Shahroudy et al [205] Feature Fusion BoW Body Unsup Cavazza et al [206] Kernelized-COV Stat Lowlv Hand …”

Section: Representations Based On Raw Joint Positionsmentioning

confidence: 99%

“…For example, Xia et al [133] partitioned the 3D space into a number of bins using a modified spherical coordinate system and counted the number of joints falling in each bin to form a 1D histogram, which is called the Histogram of 3D Joint Positions (HOJ3D). A large number of skeleton-based human representations using similar histogram encoding methods were also introduced, including Histogram of Joint Position Differences (HJPD) [142], Histogram of Oriented Velocity Vectors (HOVV) [171], and Histogram of Oriented Displacements (HOD) [209], among others [187,163,204,177,161,213]. When multi-modal skeletonbased features are involved, concatenation-based encoding is usually employed to incorporate multiple histograms into a single final feature vector [213].…”

Section: Statistics-based Encodingmentioning

confidence: 99%

Space-time representation of people based on 3D skeletal data: A review

Han

Reily

Hoff

et al. 2017

Computer Vision and Image Understanding

279

192

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Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Representations can be broadly categorized into two groups, depending on whether they use RGB-D information or 3D skeleton data. Recently, skeletonbased human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of benchmark datasets with skeleton data, and discuss potential future research directions.

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“…In [13] DTW computes a dissimilarity of movement measure by time-warping the sequences on a per sample basis by using the distance between the current reference and test sequences. The method [14] is based on histograms of action poses, extracted from MoCap data that are computed according to Hausdorff distance. The histograms are then compared with the Bhattacharyya distance and warped by a dynamic time warping process to achieve their optimal alignment.…”

Section: Approaches To Actions Recognitionmentioning

confidence: 99%

Application of Assistive Computer Vision Methods to Oyama Karate Techniques Recognition

2015

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Abstract:In this paper we propose a novel algorithm that enables online actions segmentation and classification. The algorithm enables segmentation from an incoming motion capture (MoCap) data stream, sport (or karate) movement sequences that are later processed by classification algorithm. The segmentation is based on Gesture Description Language classifier that is trained with an unsupervised learning algorithm. The classification is performed by continuous density forward-only hidden Markov models (HMM) classifier. Our methodology was evaluated on a unique dataset consisting of MoCap recordings of six Oyama karate martial artists including multiple champion of Kumite Knockdown Oyama karate. The dataset consists of 10 classes of actions and included dynamic actions of stands, kicks and blocking techniques. Total number of samples was 1236. We have examined several HMM classifiers with various number of hidden states and also Gaussian mixture model (GMM) classifier to empirically find the best setup of the proposed method in our dataset. We have used leave-one-out cross validation. The recognition rate of our methodology differs between karate techniques and is in the range of 81% ± 15% even to 100%. Our method is not limited for this class of actions but can be easily adapted to any other MoCap-based actions. The description of our approach and its evaluation are the main contributions of this paper. The results presented in this paper are effects of pioneering research on online karate action classification.

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Ongoing human action recognition with motion capture

Cited by 120 publications

References 29 publications

Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

Space-time representation of people based on 3D skeletal data: A review

Application of Assistive Computer Vision Methods to Oyama Karate Techniques Recognition

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