计算机科学 ›› 2020, Vol. 47 ›› Issue (4): 184-188.doi: 10.11896/jsjkx.190700212
刘砚, 雷印杰, 宁芊
LIU Yan, LEI Yin-jie, NING Qian
摘要: 目前,在密集场景人群计数任务中,标注真实密度图的方法是对行人头部的中心位置进行标注,并利用高斯卷积生成真实的密度分布图作为监督信息。但是,对于密集场景而言,这样的标注方式是费时、费力的,并且密集场景图片中有诸多“非受控”因素,如低分辨率、背景噪声、目标遮挡和尺度变化等。针对这一问题,提出了一种新的标注方法,即只需要知道图片中包含多少个物体,以图片中行人的数量作为监督信息。与传统的真实密度图相比,所提出的标记方法中以真实目标的数值为“弱监督”信息。实验结果表明,对于人群回归任务,利用弱监督信息对神经网络进行训练得到的模型能够较为准确地回归出图片中所包含目标的数量,从而证明了该方法的有效性。
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