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- 2018
基于卷积神经网络和密度分布特征的人数统计方法
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Abstract:
在行人监控视频中，由于行人遮挡、场景光照变化，人群分布不均等因素的影响使得现有方法难以准确统计视频中人数。针对该问题，提出一种基于卷积神经网络和密度分布特征的人数统计方法。该方法首先将场景中的人群依据密度进行划分；对稀疏人群，使用Retinex算法将场景去噪后转换至HSV空间中对行人位置进行预判，并使用栅极损失函数分块训练卷积神经网络提取行人特征，实现对遮挡行人局部位置的识别；对密集人群，提取人群密度分布特征并使用多核回归函数估计人群数量。该算法在PETS2009、UCSD等数据集上进行了测试，实验结果表明所提算法具有更好的统计精度。
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