An Incremental Dynamic Time Warping for person re-identification

Kwankhoom, Wisrut; Muneesawang, Paisarn

doi:10.1109/jcsse.2017.8025916

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…The Kinect ® sensor is a human motion tracking device for the Xbox 360 ® console from Microsoft ® . It was first intended for use in gaming systems, but many other implementation possibilities have emerged, such as human motion and feature recognition, 3D model reconstruction, robot navigation, medical applications and dance training [15][16][17].…”

Section: Kinect ® Sensormentioning

confidence: 99%

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Personal Re-identification Using Incremental Dynamic Time Warping Algorithm and Body Measuremen

Kwankhoom

Muneesawang

2022

Curr. Appl. Sci. Technol.

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Identification of unique individuals is being extensively used in security and surveillance. Gait recognition has caught the attention of computer vision researchers. This interest has been stimulated by the development of systems to automatically identify individuals. This paper presents a gait gesture recognition algorithm, using Incremental Dynamic Time Warping (IDTW) with a body measurement technique that identifies personal gait patterns recorded on video via Microsoft’s Kinect® 3D depth-sensing camera. We used the height of a person to further clarify the recognition and accurate identification of the individual. The initial results demonstrated 81.25% accuracy with our gait and height recognition algorithm. This recognition technique is ideal for high-level security requirements.

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Section: Kinect ® Sensormentioning

confidence: 99%

“…Lastly, the normalized minimum value from the newly added column of the cost matrix G is returned as the current IDTW distance, as shown in Figure 6. It allows the IDTW to calculate the distance score between two sequences faster and with greater accuracy than the classic DTW [9,[15][16][17]. P  (N+1) 3:…”

Section: J E J M =mentioning

confidence: 99%

Personal Re-identification Using Incremental Dynamic Time Warping Algorithm and Body Measuremen

Kwankhoom

Muneesawang

2022

Curr. Appl. Sci. Technol.

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“…Many distance measures such as the Euclidean distance are rather ill-suited whenever two time series are shifted in time, locally recorded with different sampling rates, warped, or have different lengths. Dynamic time warping (DTW) was originally proposed by Sakoe and Chiba (1978), and has since been the distance measure of choice in many works for time series analysis (Berndt and Clifford 1994;Keogh 2002;Ding, Trajcevski, Scheuermann, Wang, and Keogh 2008;Kwankhoom and Muneesawang 2017;Oregi, Pérez, Del Ser, and Lozano 2017;Giorgino et al 2009). DTW is capable of dealing with deformed time series by identifying the best alignment of two time series in a dynamic way.…”

Section: Introductionmentioning

confidence: 99%

“…Since then the principle of the incremental DTW computation has been applied in multiple works, as e.g. : Dixon (2005) applied it for an online algorithm to track musical performances, Mori, Uchida, Kurazume, Taniguchi, Hasegawa, and Sakoe (2006) for an algorithm to early recognize gestures, Tormene, Giorgino, Quaglini, and Stefanelli (2008) to analyze multivariate sensor readings to support neurological patients with real-time information while undergoing motor rehabilitation, Kwankhoom and Muneesawang (2017) for online algorithms which reidentify movement trajectories of persons captured with a 3D depth sensing camera, where time series matching is updated as soon as new video frames are recorded, and Oregi et al (2017) for proposing the Online-DTW (ODTW) algorithm.…”

Section: Introductionmentioning

confidence: 99%

IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping

Leodolter¹,

Plant²,

Brändle³

2021

J. Stat. Soft.

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Dynamic time warping (DTW) is a popular distance measure for time series analysis and has been applied in many research domains. This paper proposes the R package IncDTW for the incremental calculation of DTW, and based on this principle IncDTW also helps to classify or cluster time series, or perform subsequence matching and k-nearest neighbor search. DTW can measure dissimilarity between two temporal sequences which may vary in speed, with a major downside of high computational costs. Especially for analyzing live data streams, subsequence matching or calculating pairwise distance matrices, runtime intensive computations are unfavorable or can even make the analysis intractable. IncDTW tackles this problem by a vector-based implementation of the DTW algorithm to reduce the space complexity from a quadratic to a linear level in number of observations, and an incremental calculation of DTW for updating interim results to reduce the runtime complexity for online applications.We discuss the fundamental functionalities of IncDTW and apply the package to classify multivariate live stream accelerometer time series for activity recognition. Finally, comparative runtime experiments with various R and Python packages for various data analysis tasks emphasize the broad applicability of IncDTW.

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An Incremental Dynamic Time Warping for person re-identification

Cited by 2 publications

References 8 publications

Personal Re-identification Using Incremental Dynamic Time Warping Algorithm and Body Measuremen

Personal Re-identification Using Incremental Dynamic Time Warping Algorithm and Body Measuremen

IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping

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