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公开(公告)号：US20120027304A1
公开(公告)日：2012-02-02
申请号：US12845095
申请日：2010-07-28
IPC分类号： G06K9/46
CPC分类号： G06K9/00718 , G06K9/00369 , G06K9/00664 , G06K9/469 , G06K9/6201 , G06K9/6202 , G06K9/6232 , G06K9/6857
摘要： The invention provides an improved method to detect semantic attributes of human body in computer vision. In detecting semantic attributes of human body in computer vision, the invention maintains a list of semantic attributes, each of which corresponds to a human body part. A computer module then analyzes segments of a frame of a digital video to detect each semantic attribute by finding a most likely attribute for each segment. A threshold is applied to select candidate segments of the frame for further analysis. The candidate segments of the frame then go through geometric and resolution context analysis by applying the physical structure principles of a human body and by analyzing increasingly higher resolution versions of the image to verify the existence and accuracy of parts and attributes. A computer module computes a resolution context score for a lower resolution version of the image based on a weighted average score computed for a higher resolution version of the image by evaluating appearance features, geometric features, and resolution context features when available on the higher resolution version of the image. Finally, an optimal configuration step is performed via dynamic programming to select an optimal output with both semantic attributes and spatial positions of human body parts on the frame.
摘要翻译： 本发明提供了一种用于检测计算机视觉中人体语义属性的改进方法。 在检测计算机视觉中人体的语义属性时，本发明保留了语义属性的列表，每个语义属性对应于人体部分。 然后，计算机模块通过为每个段找到最可能的属性来分析数字视频的帧的段以检测每个语义属性。 应用阈值来选择帧的候选片段用于进一步分析。 然后，帧的候选片段通过应用人体的物理结构原理并通过分析图像的越来越高的分辨率版本来验证部件和属性的存在和准确性来进行几何和分辨率上下文分析。 计算机模块基于通过在更高分辨率版本上可用时评估外观特征，几何特征和分辨率上下文特征来计算针对图像的较高分辨率版本的加权平均得分，来计算图像的较低分辨率版本的分辨率上下文得分 的图像。 最后，通过动态规划执行最佳配置步骤，以选择具有框架上人体部位的语义属性和空间位置的最优输出。
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公开(公告)号：US08532390B2
公开(公告)日：2013-09-10
申请号：US12845095
申请日：2010-07-28
IPC分类号： G06K9/00
CPC分类号： G06K9/00718 , G06K9/00369 , G06K9/00664 , G06K9/469 , G06K9/6201 , G06K9/6202 , G06K9/6232 , G06K9/6857
摘要： The invention provides an improved method to detect semantic attributes of human body in computer vision. In detecting semantic attributes of human body in computer vision, the invention maintains a list of semantic attributes, each of which corresponds to a human body part. A computer module then analyzes segments of a frame of a digital video to detect each semantic attribute by finding a most likely attribute for each segment. A threshold is applied to select candidate segments of the frame for further analysis. The candidate segments of the frame then go through geometric and resolution context analysis by applying the physical structure principles of a human body and by analyzing increasingly higher resolution versions of the image to verify the existence and accuracy of parts and attributes. A computer module computes a resolution context score for a lower resolution version of the image based on a weighted average score computed for a higher resolution version of the image by evaluating appearance features, geometric features, and resolution context features when available on the higher resolution version of the image. Finally, an optimal configuration step is performed via dynamic programming to select an optimal output with both semantic attributes and spatial positions of human body parts on the frame.
摘要翻译： 本发明提供了一种用于检测计算机视觉中人体语义属性的改进方法。 在检测计算机视觉中人体的语义属性时，本发明保留了语义属性的列表，每个语义属性对应于人体部分。 然后，计算机模块通过为每个段找到最可能的属性来分析数字视频的帧的段以检测每个语义属性。 应用阈值来选择帧的候选片段用于进一步分析。 然后，帧的候选片段通过应用人体的物理结构原理并通过分析图像的越来越高的分辨率版本来验证部件和属性的存在和准确性来进行几何和分辨率上下文分析。 计算机模块基于通过在更高分辨率版本上可用时评估外观特征，几何特征和分辨率上下文特征来计算针对图像的较高分辨率版本的加权平均得分，来计算图像的较低分辨率版本的分辨率上下文得分 的图像。 最后，通过动态规划执行最佳配置步骤，以选择具有框架上人体部位的语义属性和空间位置的最优输出。
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