• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Imaging Science and Technology >Multi-Hypothesis Approach for Efficient Human Detection
【24h】

Multi-Hypothesis Approach for Efficient Human Detection

机译：高效人体检测的多假设方法

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Detection of human beings in a complex background environment is a challenging task in computer vision. Most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. In this paper, we propose a new feature extraction technique that combines three types of visual information; shape, color, and texture, and is named as the Color space Phase features with Gradient and Texture (CPGT) algorithm. Gradient concept and the phase congruency in color domain are used to localize the shape features. The Center-Symmetric Local Binary Pattern (CSLBP) approach is used to extract the texture information of the image. Fusing of these complementary features yields to capture a broad range of the human appearance details that improves the detection performance. The proposed features are formed by computing the gradient magnitude and CSLBP values for each pixel in the image with respect to its neighborhood in addition to the phase congruency of the three-color channels. Only the maximum phase congruency magnitudes are selected from the corresponding color channels. The histogram of oriented phase and gradients as well as the histogram of CSLBP values for the local regions of the image are determined and concatenated to construct the proposed descriptor. Principal Component Analysis (PCA) is performed to reduce the dimensionality of the resultant features. Several experiments were conducted to evaluate the performance of the proposed descriptor. The experimental results show that the proposed approach yields promising performance and has lower error rates when compared to several state of the art feature extraction methodologies. We observed a miss rate of 2.23% in the INRIA dataset and 2.6% in the NICTA dataset. (C) 2019 Society for Imaging Science and Technology.
机译：在复杂的背景环境中检测人类在计算机视觉中是一个具有挑战性的任务。大多数时间没有单个特征算法都足够丰富，可捕获图像中可用的所有相关信息。在本文中，我们提出了一种新的特征提取技术，结合了三种类型的视觉信息;形状，颜色和纹理，并被命名为具有梯度和纹理（CPGT）算法的颜色空间相位特征。渐变概念和彩色域中的相变量用于本地化形状功能。中心对称本地二进制模式（CSLBP）方法用于提取图像的纹理信息。这些互补特征的融合产生捕获广泛的人类外观细节，可以提高检测性能。除了三色通道的相位相会之外，通过计算图像中的每个像素的梯度幅度和CSLBP值来形成所提出的特征。仅从相应的颜色通道中选择最大相一致性幅度。确定并连接到图像的局部区域的CSLBP值的直方图以及CSLBP值的直方图，以构建所提出的描述符。进行主成分分析（PCA）以减少所产生特征的维度。进行了几个实验以评估所提出的描述符的性能。实验结果表明，与若干艺术特征提取方法相比，该方法产生了有希望的性能并具有较低的误差率。我们在Inria DataSet中观察到2.23％的错过率，并在Nicta数据集中的2.6％。 （c）2019年成像科技协会。

著录项

  • 来源
    《Journal of Imaging Science and Technology》 |2019年第2期|38-50|共13页
  • 作者

    Ragb Hussin; Asari Vijayan;

  • 作者单位

    Univ Dayton Elect & Comp Eng Vis Lab 300 Coll Pk Dayton OH 45469 USA;

    Univ Dayton Elect & Comp Eng Vis Lab 300 Coll Pk Dayton OH 45469 USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号