Real‐time motion data annotation via action string

Xiao, Jun; Zhuang, Yueting; Zhang, Hanzhi; Yang, Xiaosong; Zhang, Jianjun; Feng, Yinfu

doi:10.1002/cav.1590

Cited by 5 publications

(7 citation statements)

References 23 publications

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“…Barnachon recognizing actions. Besides, these two approaches [23,24] have shown the potential for online action recognition. Moreover, sparse representation can also be applied in motion data retrieval [25].…”

Section: Human Motion Retrievalmentioning

confidence: 99%

“…Moreover, sparse representation can also be applied in motion data retrieval [25]. Generally, to use pose/frame based features, it requires an integration step such as BOVW [22], DTW [23] or manifold embedding [10,26] to represent the whole motion clip.…”

Section: Human Motion Retrievalmentioning

confidence: 99%

“…Following a similar paradigm, Kapsouras and Nikolaidis [22] used joints orientation angles and angles forward differences as local features to create a code book and generate a Bag of Visual Word (BOVW) to represent the action. A Gaussian Mixture Model (GMM) was applied by Qi et al [23] to represent character poses, wherein the motion sequence is encoded, and then DTW and a string matching algorithm are applied for motion comparison. Barnachon recognizing actions.…”

Section: Human Motion Retrievalmentioning

confidence: 99%

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Adaptive multi-view feature selection for human motion retrieval

et al. 2016

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a b s t r a c tHuman motion retrieval plays an important role in many motion data based applications. In the past, many researchers tended to use a single type of visual feature as data representation. Because different visual feature describes different aspects about motion data, and they have dissimilar discriminative power with respect to one particular class of human motion, it led to poor retrieval performance. Thus, it would be beneficial to combine multiple visual features together for motion data representation. In this article, we present an Adaptive Multi-view Feature Selection (AMFS) method for human motion retrieval. Specifically, we first use a local linear regression model to automatically learn multiple view-based Laplacian graphs for preserving the local geometric structure of motion data. Then, these graphs are combined together with a non-negative view-weight vector to exploit the complementary information between different features. Finally, in order to discard the redundant and irrelevant feature components from the original highdimensional feature representation, we formulate the objective function of AMFS as a general trace ratio optimization problem, and design an effective algorithm to solve the corresponding optimization problem. Extensive experiments on two public human motion database, i.e., HDM05 and MSR Action3D, demonstrate the effectiveness of the proposed AMFS over the state-of-art methods for motion data retrieval. The scalability with large motion dataset, and insensitivity with the algorithm parameters, make our method can be widely used in real-world applications.

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Section: Human Motion Retrievalmentioning

confidence: 99%

Section: Human Motion Retrievalmentioning

confidence: 99%

Section: Human Motion Retrievalmentioning

confidence: 99%

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Adaptive multi-view feature selection for human motion retrieval

et al. 2016

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“…A classical Knuth-Morris-Pratt (KMP) string matching algorithm is applied and extended to compare motions. Qi et al [31] generate the 'action string' from a motion, and a string matching algorithm based on dynamic programming (DP) is used in motion matching.…”

Section: Related Workmentioning

confidence: 99%

A human motion feature based on semi-supervised learning of GMM

et al. 2014

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those motion data to create realistic motions for new characters by applying motion editing [1,2], motion synthesis [3][4][5] and motion retargeting [6] techniques has become the focus of research in the past several years. However, prior to reusing and processing the old motions, one fundamental problem of identifying and extracting similar motion clips from the database has to be solved. It is essentially a motion matching problem. The general procedure is to calculate a concise and representative feature for each motion, and then compare the similarities with all other motions in the mocap database. The efficiency and accuracy of these motion retrieval and annotation processes largely depend on the property of the used features.Most early work [7] uses textual description such as running, walking, to label existing motions in a database. It does not only involve a lot of manual work, the textual label is also too short and general to fully represent the features of each motion. Later works [8][9][10][11] use the numeric-based features and logic-based features involving the 3D coordinates of each joint in each frame. It includes too much redundant information and the 'huge' feature makes the motion matching really slow. Some recent works [12] present semantic features which better represent the essence of motions and the low dimension of features largely speeds up the motion retrieval process. In this paper, we present a new feature in this category. The work [13] has shown that a human motion clip could be described with some representative poses, which we call the 'key-pose'. Intuitively, two similar motions may share most key-poses, while the motions belonging to different motion classes may share none or only a few key-poses (as shown in Fig. 1). A good selection of key-poses can be used to represent different motion classes. The second benefit to use key-poses as a feature is, although the category of motions is infinite, the types of key-poses are relatively limited. A new category Abstract Using motion capture to create naturally looking motion sequences for virtual character animation has become a standard procedure in the games and visual effects industry. With the fast growth of motion data, the task of automatically annotating new motions is gaining an importance. In this paper, we present a novel statistic feature to represent each motion according to the pre-labeled categories of key-poses. A probabilistic model is trained with semi-supervised learning of the Gaussian mixture model (GMM). Each pose in a given motion could then be described by a feature vector of a series of probabilities by GMM. A motion feature descriptor is proposed based on the statistics of all pose features. The experimental results and comparison with existing work show that our method performs more accurately and efficiently in motion retrieval and annotation.

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“…To classify motions, researchers generally represent motion sequences as high dimensional trajectories followed by a time-warping technique, then they train a classification model to predict the classes of new motions. There are some other interesting methods attempting to transform motion capture data onto different representations such as graphs, [1][2][3] images, 4 strings, 5,6 and words. 7 Correspondingly, these changes on representation introduce new frameworks for motion capture data processing from graph theory, image processing and natural language processing.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised motion capture data segmentation based on topic model

Bao

Xie³

et al. 2021

Computer Animation & Virtual

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In this paper, we propose an unsupervised motion segmentation method based on topical model borrowed from Natural Language Processing. We apply hierarchical clustering on motion dataset to obtain a list of representative poses to constitute motion 'vocabulary'. By doing so, motion capture data can be viewed as text which comprises a sequence of motion words. We use sliding window to generate a sequence of motion documents (with overlap between consecutive motion documents). Then we use Sparse Topical Coding (STC) model to extract sparse topical codes of motion documents and conduct spectral clustering to get motion segmentations. Silhouette coefficient is used to determine the value of K (number of motion types). The results of experiments show that our method can segment motions with a very high accuracy. Our method has a strong generalization ability that also performs well on motion data which is captured by different subjects, with various motion types, even though they are from different motion dataset (HDM05 in our experiment).

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Real‐time motion data annotation via action string

Cited by 5 publications

References 23 publications

Adaptive multi-view feature selection for human motion retrieval

Adaptive multi-view feature selection for human motion retrieval

A human motion feature based on semi-supervised learning of GMM

Unsupervised motion capture data segmentation based on topic model

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