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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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Proceedings ArticleDOI
19 Mar 2013
TL;DR: Results show that combining accelerometer and gyroscope data is beneficial also for algorithms with dimensionality constraints and improves the gesture recognition rate on the authors' data set by up to 4%.
Abstract: Motivated by the addition of gyroscopes to a large number of new smart phones, we study the effects of combining accelerometer and gyroscope data on the recognition rate of motion gesture recognizers with dimensionality constraints. Using a large data set of motion gestures we analyze results for the following algorithms: Protractor3D, Dynamic Time Warping (DTW) and Regularized Logistic Regression (LR). We chose to study these algorithms because they are relatively easy to implement, thus well suited for rapid prototyping or early deployment during prototyping stages. For use in our analysis, we contribute a method to extend Protractor3D to work with the 6D data obtained by combining accelerometer and gyroscope data. Our results show that combining accelerometer and gyroscope data is beneficial also for algorithms with dimensionality constraints and improves the gesture recognition rate on our data set by up to 4%.

35 citations

Proceedings ArticleDOI
23 Mar 1992
TL;DR: A novel keyword-spotting system that combines both neural network and dynamic programming techniques is presented, which makes use of the strengths of time delay neural networks (TDNNs), which include strong generalization ability, potential for parallel implementations, robustness to noise, and time shift invariant learning.
Abstract: A novel keyword-spotting system that combines both neural network and dynamic programming techniques is presented. This system makes use of the strengths of time delay neural networks (TDNNs), which include strong generalization ability, potential for parallel implementations, robustness to noise, and time shift invariant learning. Dynamic programming models are used by this system because they have the useful capability of time warping input speech patterns. This system was trained and tested on the Stonehenge Road Rally database, which is a 20-keyword-vocabulary, speaker-independent, continuous-speech corpus. Currently, this system performs at a figure of merit (FOM) rate of 82.5%. FOM is the detection rate averaged from 0 to 10 false alarms per keyword hour. This measure is explained in detail. >

35 citations

Book ChapterDOI
07 Oct 2012
TL;DR: It is shown that by incorporating dynamics, modelling annotation/sequence specific biases, noise estimation and time warping, DPCTW outperforms state-of-the-art methods for both the aggregation of multiple, yet imperfect expert annotations as well as the alignment of affective behavior.
Abstract: Fusing multiple continuous expert annotations is a crucial problem in machine learning and computer vision, particularly when dealing with uncertain and subjective tasks related to affective behaviour. Inspired by the concept of inferring shared and individual latent spaces in probabilistic CCA (PCCA), we firstly propose a novel, generative model which discovers temporal dependencies on the shared/individual spaces (DPCCA). In order to accommodate for temporal lags which are prominent amongst continuous annotations, we further introduce a latent warping process. We show that the resulting model (DPCTW) (i) can be used as a unifying framework for solving the problems of temporal alignment and fusion of multiple annotations in time, and (ii) that by incorporating dynamics, modelling annotation/sequence specific biases, noise estimation and time warping, DPCTW outperforms state-of-the-art methods for both the aggregation of multiple, yet imperfect expert annotations as well as the alignment of affective behavior.

35 citations

Proceedings ArticleDOI
21 Oct 2013
TL;DR: The approach with skin colour based features involving utilisation of depth information of each pixel obtained by Kinect yielded 98% recognition rate, and was improved by changing gesture representation from time series to a vector containing pairwise distances between gesture samples.
Abstract: In this paper we present an approach to recognition of signed expressions based on visual sequences obtained with Kinect sensor. Two variants of time series representing the expressions are considered: the first based on skeletal images of the body, and the second describing shape and position of hands extracted as skin coloured regions. Time series characterising isolated Polish sign language words are examined using three clustering algorithms and popular clustering quality indices which reveal natural gesture data division and indicate gesture samples difficult in further recognition. Ten-fold cross-validation recognition tests for the k-nearest neighbour classifier with dynamic time warping technique are shown. Recognition rate obtained with the skeletal image based features were improved from 89% to 95% by changing gesture representation from time series to a vector containing pairwise distances between gesture samples. The approach with skin colour based features involving utilisation of depth information of each pixel obtained by Kinect yielded 98% recognition rate.

35 citations

Proceedings ArticleDOI
18 May 2012
TL;DR: A novel accelerometer based user independent hand gesture recognition algorithm which has the possibility of simpler implementation and a high recognition accuracy gives this algorithm an upper hand among competitive methods published in recent literatures.
Abstract: Hand gesture recognition is becoming increasingly popular for applications in ubiquitous computing environment. Accelerometer based methods have proven themselves to be competitive in terms of both portability and recognition accuracy. But there are only a few algorithms which have achieved moderate accuracies in user independent gesture recognition. A novel accelerometer based user independent hand gesture recognition algorithm is proposed in this paper. For validation of accuracy the test was run over 3200 samples collected from 5 users over 5 days. The overall accuracy was found to be 96.4% for user independent gesture recognition. A high recognition accuracy and possibility of simpler implementation also gives this algorithm an upper hand among competitive methods published in recent literatures. A hardware-accelerated dynamic time warping (DTW) implementation is also proposed to pave the way towards continuous gesture recognition for DTW based methods.

34 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023236
2022471
2021341
2020416
2019420
2018377