在主动模式下行走时,检测形状变化的行人

IF 0.2 Q4 SOCIAL SCIENCES, INTERDISCIPLINARY
J. Antonio, Marcelo Romero
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引用次数: 1

摘要

为行人检测器提供了主动形状(ASM)算法模型,分为训练(PDM)和调整(ASM)两个阶段。使用PDM,标记50个地标,并在137个图像(行人专用区1和行人专用区2)中提取每个行人的轮廓中的灰色轮廓,应用变化模式(PCA)。这项工作的贡献是对行人的调整和检测,尽管存在变化。最后,在每个1080×720像素的图像中,使用均方误差(MSE)度量,在原始地标和估计地标之间的误差距离中获得12.7个像素的总平均值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detección de peatones con variaciones de forma al caminar con Modelos de Forma Activa
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 (pedestrian1 and pedestrian2) 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.
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来源期刊
Ciencia Ergo-Sum
Ciencia Ergo-Sum SOCIAL SCIENCES, INTERDISCIPLINARY-
自引率
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发文量
24
审稿时长
20 weeks
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