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A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation

Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of...

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Autores principales: Liu, Xinghua, Zhao, Yunan, Yang, Lei, Ge, Shuzhi Sam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502573/
https://www.ncbi.nlm.nih.gov/pubmed/36146090
http://dx.doi.org/10.3390/s22186732
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author Liu, Xinghua
Zhao, Yunan
Yang, Lei
Ge, Shuzhi Sam
author_facet Liu, Xinghua
Zhao, Yunan
Yang, Lei
Ge, Shuzhi Sam
author_sort Liu, Xinghua
collection PubMed
description Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm.
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spelling pubmed-95025732022-09-24 A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation Liu, Xinghua Zhao, Yunan Yang, Lei Ge, Shuzhi Sam Sensors (Basel) Article Motion segmentation is one of the fundamental steps for detection, tracking, and recognition, and it can separate moving objects from the background. In this paper, we propose a spatial-motion-segmentation algorithm by fusing the events-dimensionality-preprocessing algorithm (EDPA) and the volume of warped events (VWE). The EDPA consists of depth estimation, linear interpolation, and coordinate normalization to obtain an extra dimension (Z) of events. The VWE is conducted by accumulating the warped events (i.e., motion compensation), and the iterative-clustering algorithm is introduced to maximize the contrast (i.e., variance) in the VWE. We established our datasets by utilizing the event-camera simulator (ESIM), which can simulate high-frame-rate videos that are decomposed into frames to generate a large amount of reliable events data. Exterior and interior scenes were segmented in the first part of the experiments. We present the sparrow search algorithm-based gradient ascent (SSA-Gradient Ascent). The SSA-Gradient Ascent, gradient ascent, and particle swarm optimization (PSO) were evaluated in the second part. In Motion Flow 1, the SSA-Gradient Ascent was 0.402% higher than the basic variance value, and 52.941% faster than the basic convergence rate. In Motion Flow 2, the SSA-Gradient Ascent still performed better than the others. The experimental results validate the feasibility of the proposed algorithm. MDPI 2022-09-06 /pmc/articles/PMC9502573/ /pubmed/36146090 http://dx.doi.org/10.3390/s22186732 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Xinghua
Zhao, Yunan
Yang, Lei
Ge, Shuzhi Sam
A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation
title A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation
title_full A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation
title_fullStr A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation
title_full_unstemmed A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation
title_short A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation
title_sort spatial-motion-segmentation algorithm by fusing edpa and motion compensation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9502573/
https://www.ncbi.nlm.nih.gov/pubmed/36146090
http://dx.doi.org/10.3390/s22186732
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