目标探测器的诊断误差Diagnosing Error in Object Detectors |
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课程网址: | http://videolectures.net/eccv2012_hoiem_detectors/ |
主讲教师: | Stefan Carlsson; Antonio Torralba; Derek Hoiem |
开课单位: | 伊利诺伊大学 |
开课时间: | 2012-11-12 |
课程语种: | 英语 |
中文简介: | 本文介绍了如何分析目标特性对检测性能的影响以及不同类型误报的频率和影响。特别是,我们检查遮挡、大小、纵横比、部件可见性、视点、定位错误以及与语义相似的对象、其他标记对象和背景混淆的影响。我们分析了两类探测器:Vedaldi等人。多核学习检测器和Felzenszwalb等人的不同版本。探测器。我们的研究表明,对大小、定位误差和与类似物体混淆的敏感度是最有效的误差形式。我们的分析还表明,为了获得较大的收益,需要进行多种不同的改进,使更详细的分析对识别研究的进展至关重要。通过使我们的软件和注释可用,我们使未来的研究人员可以轻松地进行类似的分析。 |
课程简介: | This paper shows how to analyze the influences of object characteristics on detection performance and the frequency and impact of different types of false positives. In particular, we examine effects of occlusion, size, aspect ratio, visibility of parts, viewpoint, localization error, and confusion with semantically similar objects, other labeled objects, and background. We analyze two classes of detectors: the Vedaldi et al. multiple kernel learning detector and different versions of the Felzenszwalb et al. detector. Our study shows that sensitivity to size, localization error, and confusion with similar objects are the most impactful forms of error. Our analysis also reveals that many different kinds of improvement are necessary to achieve large gains, making more detailed analysis essential for the progress of recognition research. By making our software and annotations available, we make it effortless for future researchers to perform similar analysis. |
关 键 词: | 计算机科学; 计算机视觉; 物体识别 |
课程来源: | 视频讲座网 |
最后编审: | 2021-06-25:liyy |
阅读次数: | 123 |