视频中基于无监督先验学习和线索融合的弱监督行人检测器训练

K. K. Htike, David C. Hogg
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引用次数: 5

摘要

在过去十年中,视频数据收集量的增长需要自动视频分析,行人检测在其中起着关键作用。使用监督式机器学习训练行人检测器需要以精确边界框的形式对行人进行繁琐的手动注释。在本文中,我们提出了一种新的弱监督算法来训练行人检测器,该算法只需要对估计的行人中心进行注释,而不需要对边界框进行注释。我们的算法利用从视频中以无监督的方式学习到的行人先验,并以一种有原则的方式将该先验与给定的弱监督信息融合。我们在公开可用的数据集上表明,我们的弱监督算法将手动注释的成本降低了4倍以上,同时实现了与使用边界框注释训练的行人检测器相似的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Weakly supervised pedestrian detector training by unsupervised prior learning and cue fusion in videos
The growth in the amount of collected video data in the past decade necessitates automated video analysis for which pedestrian detection plays a key role. Training a pedestrian detector using supervised machine learning requires tedious manual annotation of pedestrians in the form of precise bounding boxes. In this paper, we propose a novel weakly supervised algorithm to train a pedestrian detector that only requires annotations of estimated centers of pedestrians instead of bounding boxes. Our algorithm makes use of a pedestrian prior learnt in an unsupervised way from the video and this prior is fused with the given weak supervision information in a principled manner. We show on publicly available datasets that our weakly supervised algorithm reduces the cost of manual annotation by over 4 times while achieving similar performance to a pedestrian detector trained with bounding box annotations.
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