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Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model

Motion segmentation and identification of structural segments from an ensemble of human trajectories continue to be a challenge. These processes entail distinguishing and categorizing distinct motion patterns manifested by pedestrians within a group, as well as recognizing conspicuous segments that...

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Main Authors: Al-Dhamari, Ahlam, Adel Hafeezallah, Adel Hafeezallah, Abu-Bakar, Syed Abd. Rahman
Format: Conference or Workshop Item
Published: 2023
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/107893/
http://dx.doi.org/10.1145/3631991.3632040
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spelling my.utm.1078932024-10-08T06:55:31Z http://eprints.utm.my/107893/ Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model Al-Dhamari, Ahlam Adel Hafeezallah, Adel Hafeezallah Abu-Bakar, Syed Abd. Rahman TK Electrical engineering. Electronics Nuclear engineering Motion segmentation and identification of structural segments from an ensemble of human trajectories continue to be a challenge. These processes entail distinguishing and categorizing distinct motion patterns manifested by pedestrians within a group, as well as recognizing conspicuous segments that exhibit their overall motion. Accurate motion segmentation is vital in many areas, particularly computer vision, automation, and human behavior investigation and monitoring. However, owing to the complexity and irregularity of human motion, this task demands sophisticated procedures. Addressing this challenge, the Angular Gaussian Mixture Model (AGMM) is proposed in this study for visual motion segmentation. The angular features of the pedestrian trajectories are incorporated into the GMM, allowing the overall proposed framework to accurately expose the similarity between the trajectories and fulfill motion segmentation. The experimental findings conducted on the CUHK benchmark demonstrate that the proposed framework outperforms various state-of-the-art methods. The findings signify the superior performance achieved by the proposed approach in effectively and accurately segmenting motion trajectories within different crowded scenarios. Statistical evaluation approaches, such as normalized mutual information index (NMI), purity, rand index (RI), F-measure, also known as the F1-score (F1), and accuracy (ACC), were utilized employing an immense number of real-world video clips. This study has effectively laid the foundation for an essential and initial stride towards achieving comprehensive, high-level crowd behavior analysis. 2023 Conference or Workshop Item PeerReviewed Al-Dhamari, Ahlam and Adel Hafeezallah, Adel Hafeezallah and Abu-Bakar, Syed Abd. Rahman (2023) Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model. In: 5th World Symposium on Software Engineering, WSSE 2023, 22 September 2023-24 September 2023, Tokyo, Japan. http://dx.doi.org/10.1145/3631991.3632040
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Al-Dhamari, Ahlam
Adel Hafeezallah, Adel Hafeezallah
Abu-Bakar, Syed Abd. Rahman
Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model
description Motion segmentation and identification of structural segments from an ensemble of human trajectories continue to be a challenge. These processes entail distinguishing and categorizing distinct motion patterns manifested by pedestrians within a group, as well as recognizing conspicuous segments that exhibit their overall motion. Accurate motion segmentation is vital in many areas, particularly computer vision, automation, and human behavior investigation and monitoring. However, owing to the complexity and irregularity of human motion, this task demands sophisticated procedures. Addressing this challenge, the Angular Gaussian Mixture Model (AGMM) is proposed in this study for visual motion segmentation. The angular features of the pedestrian trajectories are incorporated into the GMM, allowing the overall proposed framework to accurately expose the similarity between the trajectories and fulfill motion segmentation. The experimental findings conducted on the CUHK benchmark demonstrate that the proposed framework outperforms various state-of-the-art methods. The findings signify the superior performance achieved by the proposed approach in effectively and accurately segmenting motion trajectories within different crowded scenarios. Statistical evaluation approaches, such as normalized mutual information index (NMI), purity, rand index (RI), F-measure, also known as the F1-score (F1), and accuracy (ACC), were utilized employing an immense number of real-world video clips. This study has effectively laid the foundation for an essential and initial stride towards achieving comprehensive, high-level crowd behavior analysis.
format Conference or Workshop Item
author Al-Dhamari, Ahlam
Adel Hafeezallah, Adel Hafeezallah
Abu-Bakar, Syed Abd. Rahman
author_facet Al-Dhamari, Ahlam
Adel Hafeezallah, Adel Hafeezallah
Abu-Bakar, Syed Abd. Rahman
author_sort Al-Dhamari, Ahlam
title Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model
title_short Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model
title_full Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model
title_fullStr Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model
title_full_unstemmed Motion segmentation of pedestrian trajectories Using Angular Gaussian Mixture model
title_sort motion segmentation of pedestrian trajectories using angular gaussian mixture model
publishDate 2023
url http://eprints.utm.my/107893/
http://dx.doi.org/10.1145/3631991.3632040
_version_ 1814043551728664576
score 13.211869

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