基于卷积特征的非平衡人群密度估计方法

Unbalanced Crowd Density Estimation Based on Convolutional Features

人群密度估计在智能监控领域具有重要的应用价值。大量理论和经验研究表明,基于数据驱动的深度神经网络往往优于传统的基于手工特征的方法。但是人群样本的数据规模很小,深层次的网络很难得到较优解。鉴于此,提出了3种解决方法:训练较浅的神经网络,使用预训练深度模型的全连接层特征和使用预训练深度模型的卷积-FV(Fisher Vector)特征。针对样本的不平衡性问题,提出了使用多个分类评估标准的解决方案。在标准数据集PETs2009上的实验结果表明,相比于现有的手工特征,卷积特征具有更好的效果。其次,相比于训练一个全新的卷积模型,基于迁移学习的深度卷积特征是更好的选择。另外,通过层数较少的深度模型获得的较低层特征的迁移性更好。

Crowd density estimation plays a central role in intelligent monitoring.Deep neural network usually outperforms conventional approaches based on manual features owing to its data-driven superiority.However,deep neural networks are still far from optimal solution because of the scarceness of large-scale datasets.To address this problem,this paper investigated the feasibility of several solutions which are training shallow neural network from scratch,using fully connected layer features of pretrained deep neural network and aggregating convolutional features by way of fisher vector（FV）.Aiming at the problem of unbalanced distribution,this paper further proposed several classification evaluation criteria.Comprehensive experiments were carried out on benchmark PETs2009 dataset.Results show that convolutional features outperform existing hand-crafted ones.Moreover,utilizing deep convolutional features based on transfer learning usually leads to better performance than the models trained from scratch.Finally,simpler pretrained models such as AlexNet can generalize better mobility of the lower layer features than more complicated ones such as VGGNet.