基于卷积神经网络和密度分布特征的人数统计方法

A Crowd Counting Method Based on Convolutional Neural Networks and Density Distribution Features

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。

Crowd counting is difficult to get accurate statistics due to shading,shadows and changes in crowd density.This paper presents an approach to combine the convolutional neural networks and density features map legitimately.We segment the crowd scene into many blobs according to the density.For low-density blobs,Retinex algorithm is used to denoise the scene and then the scene is transformed into HSV color space to locate the pedestrian.Convolutional neural networks are used to extract the pedestrian features with grid loss function to avoid the occlusion issue.For high density blobs,crowd density distribution features are extracted to train the multiple kernel regression models to estimate the numbers.Experiments are conducted on datasets PETS2009,UCSD.The experimental results show that the proposed method improves the accuracy to some extent in comparison with other algorithms.