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.

Indexing large human-motion databases

Abstract: Data-driven animation has become the industry standard for computer games and many animated movies and special effects In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters However, the manipulation of such data for general use and re-use is not yet a solved problem Many of the existing techniques dealing with editing motion rely on indexing for annotation, segmentation, and re-ordering of the data Euclidean distance is inappropriate for solving these indexing problems because of the inherent variability found in human motion The limitations of Euclidean distance stems from the fact that it is very sensitive to distortions in the time axis A partial solution to this problem, Dynamic Time Warping (DTW), aligns the time axis before calculating the Euclidean distance However, DTW can only address the problem of local scaling As we demonstrate in this paper, global or uniform scaling is just as important in the indexing of human motion We propose a novel technique to speed up similarity search under uniform scaling, based on bounding envelopes Our technique is intuitive and simple to implement We describe algorithms that make use of this technique, we perform an experimental analysis with real datasets, and we evaluate it in the context of a motion capture processing system The results demonstrate the utility of our approach, and show that we can achieve orders of magnitude of speedup over the brute force approach, the only alternative solution currently available

WARP: accurate retrieval of shapes using phase of Fourier descriptors and time warping distance

Abstract: Effective and efficient retrieval of similar shapes from large image databases is still a challenging problem in spite of the high relevance that shape information can have in describing image contents. We propose a novel Fourier-based approach, called WARP, for matching and retrieving similar shapes. The unique characteristics of WARP are the exploitation of the phase of Fourier coefficients and the use of the dynamic time warping (DTW) distance to compare shape descriptors. While phase information provides a more accurate description of object boundaries than using only the amplitude of Fourier coefficients, the DTW distance permits us to accurately match images even in the presence of (limited) phase shillings. In terms of classical precision/recall measures, we experimentally demonstrate that WARP can gain, say, up to 35 percent in precision at a 20 percent recall level with respect to Fourier-based techniques that use neither phase nor DTW distance.

LB_Keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures

Abstract: The matching of two-dimensional shapes is an important problem with applications in domains as diverse as biometrics, industry, medicine and anthropology. The distance measure used must be invariant to many distortions, including scale, offset, noise, partial occlusion, etc. Most of these distortions are relatively easy to handle, either in the representation of the data or in the similarity measure used. However rotation invariance seems to be uniquely difficult. Current approaches typically try to achieve rotation invariance in the representation of the data, at the expense of discrimination ability, or in the distance measure, at the expense of efficiency. In this work we show that we can take the slow but accurate approaches and dramatically speed them up. On real world problems our technique can take current approaches and make them four orders of magnitude faster, without false dismissals. Moreover, our technique can be used with any of the dozens of existing shape representations and with all the most popular distance measures including Euclidean distance, Dynamic Time Warping and Longest Common Subsequence.

Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping

Abstract: This paper discusses the development of a natural gesture user interface that tracks and recognizes in real time hand gestures based on depth data collected by a Kinect sensor. The interest space corresponding to the hands is first segmented based on the assumption that the hand of the user is the closest object in the scene to the camera. A novel algorithm is proposed to improve the scanning time in order to identify the first pixel on the hand contour within this space. Starting from this pixel, a directional search algorithm allows for the identification of the entire hand contour. The $k$ -curvature algorithm is then employed to locate the fingertips over the contour, and dynamic time warping is used to select gesture candidates and also to recognize gestures by comparing an observed gesture with a series of prerecorded reference gestures. The comparison of results with state-of-the-art approaches shows that the proposed system outperforms most of the solutions for the static recognition of sign digits and is similar in terms of performance for the static and dynamic recognition of popular signs and for the sign language alphabet. The solution simultaneously deals with static and dynamic gestures as well as with multiple hands within the interest space. An average recognition rate of 92.4% is achieved over 55 static and dynamic gestures. Two possible applications of this work are discussed and evaluated: one for interpretation of sign digits and gestures for a friendlier human-machine interaction and the other one for the natural control of a software interface.