Exploring frequency-based approaches for efficient trajectory classification-Reference-Cited by-同舟云学术

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Exploring frequency-based approaches for efficient trajectory classification

Published:2020-03-29 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 35th Annual ACM Symposium on Applied Computing
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Author:

Vicenzi Francisco¹,Petry Lucas May¹,Silva Camila Leite da¹,Alvares Luis Otavio¹,Bogorny Vania¹

Affiliation:

1. Universidade Federal de Santa Catarina (UFSC), Florianópolis, Brazil

Funder

FAPESC (Fundacao de Amparo a Pesquisa e Inovacao do Estado de Santa Catarina)

CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior)

CNPQ (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3341105.3374045

Reference18 articles.

1. Individual and Group Activity Recognition in Moving Object Trajectories;Beber Marco Aurelio;JIDM,2017

2. Friendship and mobility

3. A Rule-based Method for Discovering Trajectory Profiles

4. Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects

5. Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal

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1. UltraMovelets: Efficient Movelet Extraction for Multiple Aspect Trajectory Classification;Lecture Notes in Computer Science;2024

2. AUTOMATISE: Multiple Aspect Trajectory Data Mining Tool Library;2022 23rd IEEE International Conference on Mobile Data Management (MDM);2022-06

3. HiPerMovelets: high-performance movelet extraction for trajectory classification;International Journal of Geographical Information Science;2022-01-03

4. Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification;Intelligent Systems;2021

5. Trajectory User Linking in C-ITS Data Analysis;GLOBECOM 2020 - 2020 IEEE Global Communications Conference;2020-12

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