Unsupervised human action retrieval using salient points in 3D mesh sequences

Veinidis, Christos; Pratikakis, Ioannis; Theoharis, Theoharis

doi:10.1007/s11042-018-5855-2

Cited by 9 publications

(7 citation statements)

References 35 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The retrieval performance is ideal, as all scalar metrics are equal to 1.00 and the recall value is 1.00 for all precision values, for the two proposed methods and for the method presented in Ref. 2.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Effective Descriptors for Human Action Retrieval from 3D Mesh Sequences

Veinidis

Danelakis

Pratikakis

et al. 2019

Int. J. Image Grap.

Self Cite

View full text Add to dashboard Cite

Two novel methods for fully unsupervised human action retrieval using 3D mesh sequences are presented. The first achieves high accuracy but is suitable for sequences consisting of clean meshes, such as artificial sequences or highly post-processed real sequences, while the second one is robust and suitable for noisy meshes, such as those that often result from unprocessed scanning or 3D surface reconstruction errors. The first method uses a spatio-temporal descriptor based on the trajectories of 6 salient points of the human body (i.e. the centroid, the top of the head and the ends of the two upper and two lower limbs) from which a set of kinematic features are extracted. The resulting features are transformed using the wavelet transformation in different scales and a set of statistics are used to obtain the descriptor. An important characteristic of this descriptor is that its length is constant independent of the number of frames in the sequence. The second descriptor consists of two complementary sub-descriptors, one based on the trajectory of the centroid of the human body across frames and the other based on the Hybrid static shape descriptor adapted for mesh sequences. The robustness of the second descriptor derives from the robustness involved in extracting the centroid and the Hybrid sub-descriptors. Performance figures on publicly available real and artificial datasets demonstrate our accuracy and robustness claims and in most cases the results outperform the state-of-the-art.

show abstract

Section: Resultsmentioning

confidence: 99%

“…It is denoted that this dataset was originally introduced in Ref. 2, where a description of its construction process is given.…”

Section: Duth-arti¯cial Datasetmentioning

confidence: 99%

Effective Descriptors for Human Action Retrieval from 3D Mesh Sequences

Veinidis

Danelakis

Pratikakis

et al. 2019

Int. J. Image Grap.

Self Cite

View full text Add to dashboard Cite

show abstract

“…We collect 3D human shapes from some public datasets and manually label the genders, such as SCAPE [23], SHREC 2007 [24], TOSCA [25], AdobeData [26], Statistical Model [27], SHREC 2014 [28], SHREC 2015 [28]- [30], Breuckmann BodyScan [31], MoSh [32], MPI FAUST [33], SPRING [34], Dyna [35], MPII CAESAR [36], MPI D-FAUST [37], and DUTH [38], which are mainly aimed at registration, correspondence and other applications. The constructed 3D Human Shapes Gender Recognition Dataset (3D-HSRD for short) consists of more than one hundred thousand shapes with various appearances and postures for evaluation of the proposed method.…”

Section: Methodsmentioning

confidence: 99%

Deep Neural Networks With Distance Distributions for Gender Recognition of 3D Human Shapes

Wang

Lin

et al. 2020

IEEE Access

View full text Add to dashboard Cite

“…For a relatively large human motion data set, it is difficult to save all the edge weights of N × N, and for two samples that are far apart, it can be considered that their relationship has only a slight impact on the final retrieval result. erefore, usually, only the K-nearest neighbor information of each sample is used to construct the map, and a certain algorithm is used to adjust the edge weight or the fusion of multiple graphs and finally extract the rearranged retrieval sequence from the graph [16]. Reordering methods based on context information can be divided into global information and local information [17].…”

Section: Introductionmentioning

confidence: 99%

Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm

Bao

Yao

2020

Complexity

View full text Add to dashboard Cite

In recent years, with the rapid development of computer storage capabilities and network transmission capabilities, users can easily share their own video and image information on social networking sites, and the amount of multimedia data on the network is rapidly increasing. With the continuous increase of the amount of data in the network, the establishment of effective automated data management methods and search methods has become an increasingly urgent need. This paper proposes a retrieval method of human motion data based on motion capture in index space. By extracting key frames from the original motion to perform horizontal dimensionality reduction and defining features based on Laban motion analysis, the motion segment is subjected to vertical feature dimensionality reduction. After extracting features from the input motion segment, motion matching is performed on the index space. This paper designs the optimization method of the phased dynamic time deformation algorithm in time efficiency and analyzes the optimization method of the phased dynamic time deformation algorithm in time complexity. Considering the time efficiency redundancy, this paper optimizes the time complexity of the phased dynamic time deformation method. This improves the time efficiency of the staged dynamic time warping algorithm, making it suitable for larger-scale human motion data problems. Experiments show that the method in this paper has the advantage of speed, is more in line with the semantics of human motion, and can meet the retrieval requirements of human motion databases.

show abstract

Unsupervised human action retrieval using salient points in 3D mesh sequences

Cited by 9 publications

References 35 publications

Effective Descriptors for Human Action Retrieval from 3D Mesh Sequences

Effective Descriptors for Human Action Retrieval from 3D Mesh Sequences

Deep Neural Networks With Distance Distributions for Gender Recognition of 3D Human Shapes

Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm

Contact Info

Product

Resources

About