Topic
Dynamic time warping
About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.
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TL;DR: An approach is presented to match imaged trajectories of anatomical landmarks using semantic correspondences between human bodies using semantic corresponds to provide geometric constraints for matching actions observed from different viewpoints and performed at different rates by actors of differing anthropometric proportions.
Abstract: An approach is presented to match imaged trajectories of anatomical landmarks (e.g. hands, shoulders and feet) using semantic correspondences between human bodies. These correspondences are used to provide geometric constraints for matching actions observed from different viewpoints and performed at different rates by actors of differing anthropometric proportions. The fact that the human body has approximate anthropometric proportion allows innovative use of the machinery of epipolar geometry to provide constraints for analyzing actions performed by people of different sizes, while ensuring that changes in viewpoint do not affect matching. In addition, for linear time warps, a novel measure, constructed only from image measurements of the locations of anatomical landmarks across time, is proposed to ensure that similar actions performed at different rates are accurately matched as well. An additional feature of this new measure is that two actions from cameras moving at constant (and possibly different) velocities can also be matched. Finally, we describe how dynamic time warping can be used in conjunction with the proposed measure to match actions in the presence of nonlinear time warps. We demonstrate the versatility of our algorithm in a number of challenging sequences and applications, and report quantitative evaluation of the matching approach presented.
30 citations
TL;DR: This study proposed a framework designed for distance based time series classification which enables users to easily apply different alignment and classification methods to different time series datasets and evaluated the framework on UCR time series repository to conclude that a suitable alignment method enhances the timeseries classification performance on nearly every dataset.
30 citations
20 Jun 2009
TL;DR: Experimental results on a handwritten word retrieval task show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses C-HMMs.
Abstract: This article proposes a novel similarity measure between vector sequences. Recently, a model-based approach was introduced to address this issue. It consists in modeling each sequence with a continuous Hidden Markov Model (CHMM) and computing a probabilistic measure of similarity between C-HMMs. In this paper we propose to model sequences with semi-continuous HMMs (SC-HMMs): the Gaussians of the SC-HMMs are constrained to belong to a shared pool of Gaussians. This constraint provides two major benefits. First, the a priori information contained in the common set of Gaussians leads to a more accurate estimate of the HMM parameters. Second, the computation of a probabilistic similarity between two SC-HMMs can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which reduces significantly the computational cost. Experimental results on a handwritten word retrieval task show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses C-HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost (up to 100 times).
30 citations
TL;DR: This letter presents a profile-based wireless fingerprinting method, which mitigates positioning ambiguity by introducing short-term historical trajectories from dead-reckoning, and provides a more reliable solution especially in environments with sparse distributed access points and a more accurate initial position.
Abstract: This letter presents a profile-based wireless fingerprinting method, which mitigates positioning ambiguity by introducing short-term historical trajectories from dead-reckoning. This approach extends the fingerprint from one dimension to multiple, and thus enriches its diversity. Meanwhile, the multi-dimensional dynamic time warping method is introduced for matching multi-dimensional fingerprints with inaccurate profile lengths. Compared with the traditional single-point matching method, the profile-matching method provides a more reliable solution especially in environments with sparse distributed access points and a more accurate initial position. Indoor walking tests with two smartphones, in two buildings, and under four motion conditions illustrated that the proposed profile-matching method reduced the position errors by 11.5-21.6 %.
30 citations
03 Nov 2009
TL;DR: The objective of this study was to evaluate the various gesture recognition algorithms currently in use, after which the most suitable algorithm was optimized in order to implement it on a mobile device.
Abstract: The objective of this study was to evaluate the various gesture recognition algorithms currently in use, after which the most suitable algorithm was optimized in order to implement it on a mobile device. Gesture recognition techniques studied include hidden Markov models, artificial neural networks and dynamic time warping. A dataset for evaluating the gesture recognition algorithms was gathered using a mobile device's embedded accelerometer. The algorithms were evaluated based on computational efficiency, recognition accuracy and storage efficiency. The optimized algorithm was implemented on the mobile device to test the empirical validity of the study.
30 citations