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Detección de peatones con variaciones de forma al caminar con Modelos de Forma Activa

Pedestrian's detection with shape variations when walking with Active Shape Models
[journal article]

Antonio, Juan Alberto
Romero, Marcelo

Abstract

Se provee un detector de peatones con el algoritmo modelos de forma activa (ASM), con las etapas entrenamiento (PDM) y ajuste (ASM). Con PDM, se marcan 50 landmarks y se extraen los perfiles de grises en la silueta de cada peatón en 137 imágenes (peatón 1 y peatón 2) aplicando los modos de variación... view more

Se provee un detector de peatones con el algoritmo modelos de forma activa (ASM), con las etapas entrenamiento (PDM) y ajuste (ASM). Con PDM, se marcan 50 landmarks y se extraen los perfiles de grises en la silueta de cada peatón en 137 imágenes (peatón 1 y peatón 2) aplicando los modos de variación (PCA). El aporte de este trabajo es el ajuste y detección de un peatón a pesar de las variaciones. Al final con los resultados evaluados con leave one out en cada imagen de 1 080 × 720 pixeles y con la métrica del error cuadrático medio (MSE) se obtiene un promedio total de 12.7 pixeles en la distancia de error entre los landmarks originales y los landmarks estimados.... view less


A pedestrian detector is provided with the algorithm models of active shape (ASM), with the stages: training (PDM) and adjustment (ASM). With PDM, 50 landmarks are marked, and gray profiles are extracted in the silhouette of each pedestrian in 137 images (pedestrian 1 and pedestrian 2) applying the ... view more

A pedestrian detector is provided with the algorithm models of active shape (ASM), with the stages: training (PDM) and adjustment (ASM). With PDM, 50 landmarks are marked, and gray profiles are extracted in the silhouette of each pedestrian in 137 images (pedestrian 1 and pedestrian 2) applying the variation modes (PCA). The contribution of this work is the adjustment and detection of a pedestrian despite the variations. At the end, the results evaluated with leave one out in each 1 080 × 720 pixels image and with the mean square error (MSE) metric, a total average of 12.7 pixels is obtained in the error distance between the original landmarks and the estimated landmarks.... view less

Keywords
pedestrian

Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods

Free Keywords
active shape models; marking; adjustment; shape variations

Document language
Spanish

Publication Year
2020

Page/Pages
p. 426-440

Journal
CIENCIA ergo-sum : revista científica multidisciplinaria de la Universidad Autónoma del Estado de México, 27 (2020) 3

DOI
https://doi.org/10.30878/ces.v27n3a10

ISSN
2395-8782

Status
Published Version; peer reviewed

Licence
Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0

With the permission of the rights owner, this publication is under open access due to a (DFG-/German Research Foundation-funded) national or Alliance license.


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.