QRPC: A new qualitative model for representing motion patterns

Glez-Cabrera, Francisco Jose; Alvarez-Bravo, J.V.; Díaz, Fernando

doi:10.1016/j.eswa.2013.01.058

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…In this review, we identified the important frameworks for dealing with qualitative trajectory relationships such as QTC (Van de Weghe, 2004; Van de Weghe, Cohn, et al, 2005), heads–bodies–tails intersection (Kurata & Egenhofer, 2006), QRPC (Glez‐Cabrera et al, 2013), and TC‐6 and TC‐10 (Baryannis et al, 2018). Compared with these formalisms and frameworks, the idea behind decomposing combinatory relationships into their constructing basic relationships was totally innovative.…”

Section: Discussionmentioning

confidence: 99%

“…They could model abstract motion patterns, such as two moving objects that split and meet. Glez‐Cabrera, Álvarez‐Bravo, and Díaz (2013) introduced qualitative rectilinear projection calculus (QRPC), a new representation model based on planar trajectories for describing motion patterns qualitatively. They could present formally how the qualitative representation in their model can be used to analyze and describe the relative motion of two objects.…”

Section: Introductionmentioning

confidence: 99%

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Automatic extraction of sophisticated qualitative trajectory relationships between two moving objects in space‐time

Vahidnia

2020

Transactions in GIS

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A comparison of moving objects’ trajectories in space‐time based on descriptive and qualitative interpretations is useful in many GIS‐related studies. Qualitative relationships help to answer questions such as “Do the objects meet each other?” or “Do the objects move towards or away from each other?” Questions about moving objects can be efficiently addressed using quantitative and computational techniques such as data mining of trajectory datasets but expressed in terms of qualitative relationships. It is, however, difficult to recognize these qualitative relationships when the trajectories are large and consist of many parts, where a sophisticated qualitative relationship can be formed from the aggregation of simple ones. For this reason, this study introduces two classes of trajectory relationships: initial qualitative trajectory relationships (IQTR) and combinatory qualitative trajectory relationships (CQTR). It also suggests effective algorithmic approaches to automatically retrieve them. The proposed algorithm for IQTR using min‐heap and linked‐list data structure could effectively reduce the time complexity from O(n2) to O(n log n). For CQTRs, a divide‐and‐conquer and recursive technique and a linked‐path structure again managed to reduce the time complexity from O(n2) to O(n log n). Finally, experiments on large datasets suggest the capability of the proposed methods in terms of performance and stability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic extraction of sophisticated qualitative trajectory relationships between two moving objects in space‐time

Vahidnia

2020

Transactions in GIS

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“…From the beginning, qualitative representations were predominantly applied to temporal or spatial situations, but not specifically to moving objects. As an illustration, Dylla et al (2017) rated only three out of about 40 surveyed representations as motion representations: the family of representations QTC (Delafontaine, Cohn, & Van de Weghe, 2011;Van de Weghe, 2004), RfDL-3-12 (Kurata &Shi, 2008), andQRPC (Glez-Cabrera, Álvarez-Bravo, &Díaz, 2013); and, actually, RfDL-3-12 does not deal with moving objects, but with the finished path the object has described which is related to the static background.…”

Section: Qualitative Representations Of Motion Scenesmentioning

confidence: 99%

Stories in the Mind? The Role of Story‐Based Categorizations in Motion Classification

Papenmeier,

Purcalla Arrufi,

Kirsch

2023

Cognitive Science

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Categorization is fundamental for spatial and motion representation in both the domain of artificial intelligence and human cognition. In this paper, we investigated whether motion categorizations designed in artificial intelligence can inform human cognition. More concretely, we investigated if such categorizations (also known as qualitative representations) can inform the psychological understanding of human perception and memory of motion scenes. To this end, we took two motion categorizations in artificial intelligence, Motion‐RCC and Motion‐OPRA1, and conducted four experiments on human perception and memory. Participants viewed simple motion scenes and judged the similarity of transformed scenes with this reference scene. Those transformed scenes differed in none, one, or both Motion‐RCC and Motion‐OPRA1 categories. Importantly, we applied an equal absolute metric change to those transformed scenes, so that differences in the similarity judgments should be due only to differing categories. In Experiments 1a and 1b, where the reference stimulus and transformed stimuli were visible at the same time (perception), both Motion‐OPRA1 and Motion‐RCC influenced the similarity judgments, with a stronger influence of Motion‐OPRA1. In Experiments 2a and 2b, where participants first memorized the reference stimulus and viewed the transformed stimuli after a short blank (memory), only Motion‐OPRA1 had marked influences on the similarity judgments. Our findings demonstrate a link between human cognition and these motion categorizations developed in artificial intelligence. We argue for a continued and close multidisciplinary approach to investigating the representation of motion scenes.

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“…An overview of representations of motion is found in a survey by Dylla et al [10]: in a total of 40 representations surveyed, they classify three as representations of motion: QRPC [13], RfDL-3-12 [15], and, the most used, QTC [26]. QTC refers to a varied family of representations characterised by suffix [7], e.g., QTC C1 .…”

Section: Qualitative Representations Of Motionmentioning

confidence: 99%

Motion categorisation: Representing velocity qualitatively

Arrufi

Kirsch

2018

Cognitive Systems Research

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Categorising is arguably one of the first steps in cognition, because it enables high-level cognitive processing. For a similar reason, categorising is a first step-a preprocessing step-in artificial intelligence, specifically in decision-making, reasoning, and natural language processing. In this paper we categorise the motion of entities. Such categorisations, also known as qualitative representations, represent the preprocessing step for navigation problems with dynamical obstacles. As a central result, we present a general method to generate categorisations of motion based on categorisations of space. We assess its general validity by generating two categorisations of motion from two different spatial categorisations. We show examples of how the categorisations of motion describe and control trajectories. And we establish its soundness in cognitive and mathematical principles.

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QRPC: A new qualitative model for representing motion patterns

Cited by 11 publications

References 25 publications

Automatic extraction of sophisticated qualitative trajectory relationships between two moving objects in space‐time

Automatic extraction of sophisticated qualitative trajectory relationships between two moving objects in space‐time

Stories in the Mind? The Role of Story‐Based Categorizations in Motion Classification

Motion categorisation: Representing velocity qualitatively

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