A Segmented Template Optimization Using the Frechet Distance

Chen, Peibo; Xu, Ke; Li, Gang; Wan, Jianwei

doi:10.1109/iscid.2016.1102

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Among shape-based methods, another commonly used technique is the Fréchet distance, which originates from the problem associated with the shortest leash length required when walking a dog, i.e., the shortest distance required for the intersection of two curves. Therefore, unlike the Hausdorff distance and OWD, the Fréchet distance considers the time relationship between the trajectory points, such that the resulting trajectories in the same and opposite directions are highly different [42][43][44]. However, as the distance between curves is difficult to obtain, the discrete Fréchet distance is now more commonly used, i.e., calculating the maximum value of the minimum distance between discrete point pairs [45,46].…”

Section: Related Researchmentioning

confidence: 99%

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Nie

Chen

Xia

et al. 2021

IJGI

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Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

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Section: Related Researchmentioning

confidence: 99%

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Nie

Chen

Xia

et al. 2021

IJGI

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“…Figure 1 shows an example of the Fréchet distance between two trajectories. Previous studies have shown that the Fréchet distance contains the temporal relationship between the interior nodes [6]. Namely, the structure of the nodes inside the trajectory is taken into consideration in the computation process, which can more accurately describe the similarity between the trajectories.…”

Section: Research Statusmentioning

confidence: 99%

An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold

Guo

Xiong

et al. 2017

IJGI

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Abstract:The processing and analysis of trajectories are the core of many location-based applications and services, while trajectory similarity is an essential concept regularly used. To address the time-consuming problem of similarity query, an efficient algorithm based on Fréchet distance called Ordered Coverage Judge (OCJ) is proposed, which could realize the filtering query with a given Fréchet distance threshold on large-scale trajectory datasets. The OCJ algorithm can obtain the result set quickly by a two-step operation containing morphological characteristic filtering and ordered coverage judgment. The algorithm is expedient to be implemented in parallel for further increases of speed. Demonstrated by experiments over real trajectory data in a multi-core hardware environment, the new algorithm shows favorable stability and scalability besides its higher efficiency in comparison with traditional serial algorithms and other Fréchet distance algorithms.

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Local frechet distance in specific emitter identification

Chen

et al. 2017

2017 IEEE 9th International Conference on Communication Software and Networks (ICCSN)

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A Segmented Template Optimization Using the Frechet Distance

Cited by 4 publications

References 6 publications

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold

Local frechet distance in specific emitter identification

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