Real-Time Skeleton-Tracking-Based Human Action Recognition Using Kinect Data

Papadopoulos, Georgios Th.; Αξενόπουλος, Απόστολος; Daras, Petros

doi:10.1007/978-3-319-04114-8_40

Cited by 123 publications

(68 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the use of the skeleton needs pre-processing, skeleton modality remains more suited to fast recognition. 74.2 9.06 HON4D (Oreifej and Liu, 2013) 89.3 76.6 17.51 SNV (Yang and Tian, 2014) 94.27 76.65 271.3 Actionlet* (Wang et al, 2012a) 87.1 69.7 0.139 LARP (Vemulapalli et al, 2014) 96.00 88.1 10.51 KSC 90 Indeed, according to (Papadopoulos et al, 2014), skeleton extraction process takes around 45ms per frame. For an action of 30 frames (very reasonable length for an action), the necessary time to extract a skeleton sequence is equal to nearly 1,35 s.…”

Section: A Trade-off Between Computational Latency and Recognition Acmentioning

confidence: 99%

An extension of kernel learning methods using a modified Log-Euclidean distance for fast and accurate skeleton-based Human Action Recognition

Ghorbel

Boonært

Boutteau

et al. 2018

Computer Vision and Image Understanding

View full text Add to dashboard Cite

In this article, we introduce a fast, accurate and invariant method for RGB-D based human action recognition using a Hierarchical Kinematic Covariance (HKC) descriptor.Recently, non singular covariance matrices of pattern features which are elements of the space of Symmetric Definite Positive (SPD) matrices, have been proven to be very efficient descriptors in the field of pattern recognition.However, in the case of action recognition, singular covariance matrices cannot be avoided because the dimension of features could be higher than the number of samples. Such covariance matrices (non singular and singular) belong to the space of Symmetric Positive semi-Definite (SPsD) matrices.Thus, in order to classify actions, we propose to adapt kernel methods such as Support Vector Machines (SVM) and Multiple Kernel Learning (MKL) to the space of SPsD matrices by using a perturbed Log-Euclidean distance (Arsigny et al., 2006). The mathematical validity of this perturbed distance (called Modified Log-Euclidean distance) for SPsD is therefore studied.The offline experiments are conducted on three challenging benchmarks, namely MSRAction3D, UTKinect and Multiview3D datasets. A fair comparison demonstrates that our approach competes with state-of-the-art methods in terms of accuracy and computational latency. Finally, our method is extended to an online scenario and experiments on MSRC12 prove the efficiency of this extension. (Enjie Ghorbel) can be found in (Poppe, 2010;Weinland et al., 2011). Unfortunately, these methods suffer from some limitations: their performance is negatively affected by occlusions, view-point variation, illumination changes and body segmentation.With the availability of low-cost RGB-D cameras, a renewed interest for action recognition has been observed. Additionally to the classical RGB images, this kind of camera also provides depth images. Furthermore, the recent algorithm proposed by Shotton et al. (2013) allows the real-time human skeleton extraction from depth maps. Thus, new methods (Rahmani et al., 2016;Amor et al., 2016;Brun et al., 2016;Liu et al., 2016) have been pro-

show abstract

Section: A Trade-off Between Computational Latency and Recognition Acmentioning

confidence: 99%

An extension of kernel learning methods using a modified Log-Euclidean distance for fast and accurate skeleton-based Human Action Recognition

Ghorbel

Boonært

Boutteau

et al. 2018

Computer Vision and Image Understanding

View full text Add to dashboard Cite

show abstract

“…Moreover, Xia et al [25] utilize histograms of 3D joint locations (HOJ3D) as a compact representation of human postures. The spherical angles between selected joints, along with the respective angular velocities, are calculated in [15].…”

Section: Previous Work On 3d Action Recognitionmentioning

confidence: 99%

Global Flow and Temporal-Shape Descriptors for Human Action Recognition from 3D Reconstruction Data

Papadopoulos

Daras

2017

Machine Learning and Data Mining in Pattern Recognition

Self Cite

View full text Add to dashboard Cite

In this paper, global-level view-invariant descriptors for human action recognition using 3D reconstruction data are proposed. 3D reconstruction techniques are employed for addressing two of the most challenging issues related to human action recognition in the general case, namely view-variance and the presence of (self-) occlusions. Initially, a set of calibrated Kinect sensors are employed for producing a 3D reconstruction of the performing subjects. Subsequently, a 3D flow field is estimated for every captured frame. For performing action recognition, a novel global 3D flow descriptor is introduced, which achieves to efficiently encode the global motion characteristics in a compact way, while also incorporating spatial distribution related information. Additionally, a new global temporal-shape descriptor that extends the notion of 3D shape descriptions for action recognition, by including temporal information, is also proposed. The latter descriptor efficiently addresses the inherent problems of temporal alignment and compact representation, while also being robust in the presence of noise. Experimental results using public datasets demonstrate the efficiency of the proposed approach.

show abstract

“…Indeed, RGB-D (Red Green Blue Depth) cameras provide an additional modality known as depth maps. Furthermore, with the work of Shotton et al [1], it became feasible to extract relatively accurate skeletons from depth maps in real-time (around 45ms for skeleton extraction per frame according to [3]). Although motion capture systems provide more accurate skeletons, the RGB-D cameras remain an interesting alternative given their lower cost.…”

Section: Introductionmentioning

confidence: 99%

A fast and accurate motion descriptor for human action recognition applications

Ghorbel

Boutteau

Bonnaert³

et al. 2016

2016 23rd International Conference on Pattern Recognition (ICPR)

View full text Add to dashboard Cite

Abstract-With the availability of the recent human skeleton extraction algorithm introduced by Shotton et al. [1], an interest for skeleton-based action recognition methods has been renewed. Despite the importance of the low-latency aspect in applications, it can be noted that the majority of recent approaches has not been evaluated in terms of computational cost. In this paper, a novel fast and accurate human action descriptor named Kinematic Spline Curves (KSC) is introduced. This descriptor is built by interpolating the kinematics of joints (position, velocity and acceleration). To overcome the anthropometric and the execution rate variabilities, we respectively propose the use of a skeleton normalization and a temporal normalization. For this purpose, a new temporal normalization method based on the Normalized Accumulated kinetic Energy (NAE) of the human skeleton is suggested. Finally, the classification step is performed using a linear Support Vector Machine (SVM). Experimental results on challenging benchmarks show the efficiency of our approach in terms of recognition accuracy and computational latency.

show abstract

Real-Time Skeleton-Tracking-Based Human Action Recognition Using Kinect Data

Cited by 123 publications

References 13 publications

An extension of kernel learning methods using a modified Log-Euclidean distance for fast and accurate skeleton-based Human Action Recognition

An extension of kernel learning methods using a modified Log-Euclidean distance for fast and accurate skeleton-based Human Action Recognition

Global Flow and Temporal-Shape Descriptors for Human Action Recognition from 3D Reconstruction Data

A fast and accurate motion descriptor for human action recognition applications

Contact Info

Product

Resources

About