Knowledge discovery in choreographic data using Relative Motion matrices and Dynamic Time Warping

Chavoshi, Seyed Hossein; Baets, Bernard De; Neutens, Tijs; Ban, Hyowon; Ahlqvist, Ola; Tré, Guy De; Weghe, Nico Van de

doi:10.1016/j.apgeog.2013.12.007

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…Moreover, although student 1 showed that he could control the relative distance between his head and right hand over time, he could not manage to control the relative directions of his movements in a manner similar to that of the teacher. The results of this approach are comparable with those in [ 62 ].…”

Section: Methodssupporting

confidence: 83%

“…Researchers have focused on different aspects in this area, including analysing the sequential aspects within the spatial and temporal dimensions of movement data (e.g., [ 39 , 52 , 63 , 64 ]). For example, in [ 62 ], key parameters that characterise the movement of objects, the so-called movement parameters (MPs) such as speed, acceleration, direction, and derived from the trajectories of objects were taken into account for finding similar trajectories. In [ 62 ], sequences of class labels as symbolic representation of MPs for the similarity measure were compared.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in [ 62 ], key parameters that characterise the movement of objects, the so-called movement parameters (MPs) such as speed, acceleration, direction, and derived from the trajectories of objects were taken into account for finding similar trajectories. In [ 62 ], sequences of class labels as symbolic representation of MPs for the similarity measure were compared. In this section, we compare our approach to two well-known techniques, namely the Relative Motion (REMO) and the Dynamic Time Warping (DTW).…”

Section: Discussionmentioning

confidence: 99%

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Exploring Dance Movement Data Using Sequence Alignment Methods

et al. 2015

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Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers.

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Section: Methodssupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Exploring Dance Movement Data Using Sequence Alignment Methods

et al. 2015

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“…In their work on dance sequence analysis, Chavoshi et al used both DTW with real-valued features [25], and QT C with sequence alignment [18], as analysis tools. They present a discursive comparison of DTW and sequence alignment methods in [18], but favour sequence alignment for their QT C approach because it is easy to interpret visually, and can be used to align multiple sequences.…”

Section: Similarity Measuresmentioning

confidence: 99%

Pair-Activity Analysis From Video Using Qualitative Trajectory Calculus

AlZoubi

Al-Diri

Pike

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-The automated analysis of interacting objects or people from video has many uses, including the recognition of activities, and identification of prototypical or unusual behaviors. Existing techniques generally use temporal sequences of quantifiable real-valued features, such as object position or orientation; however, more recently, qualitative representations have been proposed. In this paper we present a novel and robust qualitative method which can be used both for classification and clustering of pair-activities. We use Qualitative Trajectory Calculus (QT C) to represent the relative motion between two objects, and encodes their interactions as a trajectory of QT C states. A key element is a general and robust means of determining the sequence similarity, which we term Normalized Weighted Sequence Alignment; we show that this is an effective metric for both recognition and clustering problems. We have evaluated our method across three different datasets, and shown that it out-performs state of the art quantitative methods, achieving an error rate of no more than 4.1% for recognition, and cluster purities higher than 90%. Our motivation originates from an interest in automated analysis of animal behaviors, and we present a comprehensive video dataset of fish behaviors (Gasterosteus aculeatus), collected from lab-based experiments.

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“…During the past two decades, the increasing availability and affordability of these data sets have aroused a burgeoning interest among (geographical) information scientists, who have steadily begun to develop and implement tools to discover, aggregate and cluster meaningful patterns of individual or group behavior in space-time (e.g., [7][8][9][10][11][12][13]). One specific area of interest concerns the line of inquiry that has developed qualitative formalisms to reason about moving objects.…”

Section: Introductionmentioning

confidence: 99%

Movement Pattern Analysis Based on Sequence Signatures

Chavoshi

Baets

Neutens

et al. 2015

IJGI

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Increased affordability and deployment of advanced tracking technologies have led researchers from various domains to analyze the resulting spatio-temporal movement data sets for the purpose of knowledge discovery. Two different approaches can be considered in the analysis of moving objects: quantitative analysis and qualitative analysis. This research focuses on the latter and uses the qualitative trajectory calculus (QTC), a type of calculus that represents qualitative data on moving point objects (MPOs), and establishes a framework to analyze the relative movement of multiple MPOs. A visualization technique called sequence signature (SESI) is used, which enables to map QTC patterns in a 2D indexed rasterized space in order to evaluate the similarity of relative movement patterns of multiple MPOs. The applicability of the proposed methodology is illustrated by means of two practical examples of interacting MPOs: cars on a highway and body parts of a samba dancer. The results show that the proposed method can be effectively used to analyze interactions of multiple MPOs in different domains.

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Knowledge discovery in choreographic data using Relative Motion matrices and Dynamic Time Warping

Cited by 9 publications

References 16 publications

Exploring Dance Movement Data Using Sequence Alignment Methods

Exploring Dance Movement Data Using Sequence Alignment Methods

Pair-Activity Analysis From Video Using Qualitative Trajectory Calculus

Movement Pattern Analysis Based on Sequence Signatures

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