How It Flies and Why It Flies? Volleyball Trajectory Segmentation and Classification

2021 ◽  
pp. 1-1
Author(s):  
Chen-Ni Chen ◽  
Wei-Ta Chu
Keyword(s):  
Trajectory Segmentation

GRASP-UTS: an algorithm for unsupervised trajectory segmentation

2014 ◽  
Vol 29 (1) ◽  
pp. 46-68 ◽  
Author(s):  
Amílcar Soares Júnior ◽  
Bruno Neiva Moreno ◽  
Valéria Cesário Times ◽  
Stan Matwin ◽  
Lucídio dos Anjos Formiga Cabral
Keyword(s):  
Trajectory Segmentation

scholarly journals Application of Artificial Intelligence in an Unsupervised Algorithm for Trajectory Segmentation Based on Multiple Motion Features

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Wenjin Xu ◽  
Shaokang Dong
Keyword(s):  
Artificial Intelligence ◽  
Minimum Description Length ◽  
Location Based Services ◽  
Global Perspective ◽  
Network Location ◽  
Pearson Coefficient ◽  
Motion Features ◽  
Harmonic Average ◽  
Artificial Intelligence Algorithm ◽  
Trajectory Segmentation

With the development of the wireless network, location-based services (e.g., the place of interest recommendation) play a crucial role in daily life. However, the data acquired is noisy, massive, it is difficult to mine it by artificial intelligence algorithm. One of the fundamental problems of trajectory knowledge discovery is trajectory segmentation. Reasonable segmentation can reduce computing resources and improvement of storage effectiveness. In this work, we propose an unsupervised algorithm for trajectory segmentation based on multiple motion features (TS-MF). The proposed algorithm consists of two steps: segmentation and mergence. The segmentation part uses the Pearson coefficient to measure the similarity of adjacent trajectory points and extract the segmentation points from a global perspective. The merging part optimizes the minimum description length (MDL) value by merging local sub-trajectories, which can avoid excessive segmentation and improve the accuracy of trajectory segmentation. To demonstrate the effectiveness of the proposed algorithm, experiments are conducted on two real datasets. Evaluations of the algorithm’s performance in comparison with the state-of-the-art indicate the proposed method achieves the highest harmonic average of purity and coverage.

scholarly journals Cluster-based trajectory segmentation with local noise

2018 ◽  
Vol 32 (4) ◽  
pp. 1017-1055 ◽  
Author(s):  
Maria Luisa Damiani ◽  
Fatima Hachem ◽  
Hamza Issa ◽  
Nathan Ranc ◽  
Paul Moorcroft ◽  
...  
Keyword(s):  
Trajectory Segmentation

scholarly journals Short Trajectory Segmentation with 1D UNET Framework: Application to Secretory Vesicle Dynamics

2020 ◽  
Author(s):  
Mariia Dmitrieva ◽  
Joel Lefebvre ◽  
Kristofer delas Penas ◽  
Helen L Zenner ◽  
Jennifer Richens ◽  
...  
Keyword(s):  
Secretory Vesicle ◽  
Vesicle Dynamics ◽  
Trajectory Segmentation

scholarly journals A feature extraction based trajectory segmentation approach based on multiple movement parameters

2020 ◽  
Vol 88 ◽  
pp. 103394
Author(s):  
Zahedeh Izakian ◽  
M. Saadi Mesgari ◽  
Robert Weibel
Keyword(s):  
Feature Extraction ◽  
Segmentation Approach ◽  
Movement Parameters ◽  
Trajectory Segmentation

scholarly journals A Fast Unsupervised Approach for Multi-Modality Surgical Trajectory Segmentation

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 56411-56422 ◽  
Author(s):  
Hongfa Zhao ◽  
Jiexin Xie ◽  
Zhenzhou Shao ◽  
Ying Qu ◽  
Yong Guan ◽  
...  
Keyword(s):  
Unsupervised Approach ◽  
Trajectory Segmentation
Sign in / Sign up

Export Citation Format

Share Document