计算机科学 ›› 2018, Vol. 45 ›› Issue (8): 236-241.doi: 10.11896/j.issn.1002-137X.2018.08.042
曲佳, 时增林, 叶阳东
QU Jia, SHI Zeng-lin, YE Yang-dong
摘要: 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。
中图分类号:
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