Retrieving Similar Movements in Motion Capture Data

Sedmidubský, Jan; Valčík, Jakub

doi:10.1007/978-3-642-41062-8_33

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…We handpicked 9 out of 119 joints (neck, left(L)/right(R) shoulders, L/R elbows, L/R hip joints, and L/R knees). For each frame in an animation we calculated the skeleton angles at these joints [10] and encoded the differences between the minimum and the maximum values for the angles during the whole animation sequence as a 9-dimensional feature vector. The dataset will be available at our web site 1 .…”

Section: Datasetsmentioning

confidence: 99%

CRMActive

Chatterjee

Leuski

2015

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

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Conventional multimedia annotation/retrieval systems such as Normalized Continuous Relevance Model (NormCRM) [7] require a fully labeled training data for a good performance. Active Learning, by determining an order for labeling the training data, allows for a good performance even before the training data is fully annotated. In this work we propose an active learning algorithm, which combines a novel measure of sample uncertainty with a novel clustering-based approach for determining sample density and diversity and integrate it with NormCRM. The clusters are also iteratively refined to ensure both feature and label-level agreement among samples. We show that our approach outperforms multiple baselines both on a new, open dataset and on the popular TRECVID corpus at both the tasks of annotation and text-based retrieval of videos.

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Section: Datasetsmentioning

confidence: 99%

CRMActive

Chatterjee

Leuski

2015

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

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“…Similarity of motions is then determined by the DTW function that exploits the L 1 metric as the local cost measure for comparing particular pose vectors. Another system for sub‐motion retrieval is proposed by Sedmidubsky et al . This approach does not already require a query motion to be specified by several positive examples.…”

Section: Case Studiesmentioning

confidence: 99%

Assessing similarity models for human‐motion retrieval applications

Valčík

Sedmidubský

Zezula

2015

Computer Animation & Virtual

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The development of motion capturing devices poses new challenges in the exploitation of human-motion data for various application fields, such as computer animation, visual surveillance, sports, or physical medicine. Recently, a number of approaches dealing with motion data have been proposed, suggesting characteristic motion features to be extracted and compared on the basis of similarity. Unfortunately, almost each approach defines its own set of motion features and comparison methods; thus, it is hard to fairly decide which similarity model is the most suitable for a given kind of human-motion retrieval application. To cope with this problem, we propose the human motion model evaluator, which is a generic framework for assessing candidate similarity models with respect to the purpose of the target application. The application purpose is specified by a user in form of a representative sample of categorized motion data. Respecting such categorization, the similarity models are assessed from the effectiveness and efficiency points of view using a set of space-complexity, information-retrieval, and performance measures. The usability of the framework is demonstrated by case studies of three practical examples of retrieval applications focusing on recognition of actions, detection of similar events, and identification of subjects.

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“…, u j of C with nonvanishing eigenvalues. (10) For vectorial data, the same effect can be obtained by deleting the null space from the data vectors in the first place employing the principal component analysis (PCA), as a very popular preprocessing approach. However, the reformulation as matrix regularization has the benefit that this principle can directly be transferred to more general data such as the alignment vectors D ij , as we see in the following.…”

Section: Metric Regularizationmentioning

confidence: 99%

“…Within such systems, distance-based methods are often used for the initial analysis or motion retrieval [9,10,11]. Due to its capability of adjusting to different durations, dynamic time warping (DTW) constitutes the by far most popular dissimilarity measure in this context [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Efficient metric learning for the analysis of motion data

Hosseini¹,

Hammer²

2015

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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We investigate metric learning in the context of dynamic time warping (DTW), the by far most popular dissimilarity measure used for the comparison and analysis of motion capture data. While metric learning enables a problem-adapted representation of data, the majority of methods has been proposed for vectorial data only. In this contribution, we extend the popular principle offered by the large margin nearest neighbors learner (LMNN) to DTW by treating the resulting component-wise dissimilarity values as features. We demonstrate that this principle greatly enhances the classification accuracy in several benchmarks. Further, we show that recent auxiliary concepts such as metric regularization can be transferred from the vectorial case to component-wise DTW in a similar way. We illustrate that metric regularization constitutes a crucial prerequisite for the interpretation of the resulting relevance profiles.

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Retrieving Similar Movements in Motion Capture Data

Cited by 5 publications

References 5 publications

CRMActive

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Assessing similarity models for human‐motion retrieval applications

Efficient metric learning for the analysis of motion data

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