Unsupervised Learning Motion Models Using Dynamic Time Warping

Kulbacki, Marek; Segen, Jakub; Bąk, Artur

doi:10.1007/978-3-7908-1777-5_23

Cited by 20 publications

(4 citation statements)

References 3 publications

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“…Thus, Xie and Wiltgen [13] suggest extending the information contained in global features of the series such as the values in DTW with local shape characteristics as depicted by DDTW. The idea of combining DTW and DDTW can also be found in ten Holt et al [16], Kulbacki and Bak [17], Benedikt et al [18], Górecki and Łuczak [19], Łuczak [20]. The approaches differ firstly in whether the contribution of DTW and DDTW towards the outcome is respectively weighted and, secondly, in the way the global feature and local feature is calculated.…”

Section: Relevant Adaptions To Dtwmentioning

confidence: 99%

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships

Stübinger

Walter

2022

Sensors

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This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.

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Section: Relevant Adaptions To Dtwmentioning

confidence: 99%

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships

Stübinger

Walter

2022

Sensors

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“…Subsequent works, however, used DDTW and showed its effectiveness in practice (recently Luan et al 2010;Mokhtar et al 2010). On the basis of this method Kulbacki et al 2002 created a measure which took into account the point-to-point distance between time series. The measure of the distance between time series was the product of the ED between two time series and the square of the difference of the estimated derivatives.…”

Section: Related Work and New Contributionmentioning

confidence: 99%

“…VDDTW-Value-Derivative DTW (Kulbacki et al 2002)-DTW distance that compares both functions and derivatives and combines them multiplicatively:…”

Section: Comparison With Related Workmentioning

confidence: 99%

Using derivatives in time series classification

Górecki

Łuczak

2012

Data Min Knowl Disc

124

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Over recent years the popularity of time series has soared. Given the widespread use of modern information technology, a large number of time series may be collected during business, medical or biological operations, for example. As a consequence there has been a dramatic increase in the amount of interest in querying and mining such data, which in turn has resulted in a large number of works introducing new methodologies for indexing, classification, clustering and approximation of time series. In particular, many new distance measures between time series have been introduced. In this paper, we propose a new distance function based on a derivative. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than point-to-point function comparison. The new distance is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 20 time series datasets from a wide variety of application domains. Our experiments show that our method provides a higher quality of classification on most of the examined datasets.

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“…In [3] and [4] DTW is applied for the reduced pose descriptors of motion capture data and binary silhouettes of CASIA dataset, respectively. In [5] the unsupervised learning is performed on the basis of the DTW distance function.…”

Section: Introductionmentioning

confidence: 99%

Gait Identification Based on MPCA Reduction of a Video Recordings Data

Michalczuk

Świtoński

Josiński

et al. 2012

Computer Vision and Graphics

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Abstract. The scope of this article is gait identification of individuals on the basis of reduced sequences of video recordings data. The gait sequences are considered to be the 3rd-order tensors and its dimensionality is reduced by Multilinear Principal Component Analysis with different values of variation covered. Reduced gait descriptors are identified by the supervised classifiers: Naive Bayes and Nearest Neighbor. CASIA Gait Database 'dataset A' is chosen to verify the proposed method. The obtained results are promising. For the Naive Bayes and attributes discretization almost 99% of classification accuracy is achieved, which means only one misclassified gait out of eighty validated.

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Unsupervised Learning Motion Models Using Dynamic Time Warping

Cited by 20 publications

References 3 publications

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships

Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships

Using derivatives in time series classification

Gait Identification Based on MPCA Reduction of a Video Recordings Data

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