一个扩展的卷积神经网络,用于跨层的上下文信息,用于拥挤的人群计数
Zhiqiang Zhao1,2, Peihong Ma1, Meng Jia1,2
1The School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
这项研究引入了一个新的群众计数网络,它使用了深度和浅度的功能. 拟议的跨层次上下文信息提取网络 (CL-DCNN) 提高了人群密度估计的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 人工智能的人工智能
背景情况:
- 群众计数对于许多应用程序至关重要,卷积神经网络 (CNN) 显示出前景.
- 现有的基于CNN的方法往往忽略了浅特征的意义,主要关注深度特征图.
- 这种限制阻碍了复杂人群密度估计任务的最佳性能.
研究的目的:
- 提出一个新的网络,跨层次的上下文信息提取网络 (CL-DCNN),用于人群计数.
- 通过提取跨层次的上下文信息,有效地整合深层和浅层特征.
- 为了提高人群计数模型的准确性和稳定性.
主要方法:
- 基于扩展卷积神经网络的方法 (CL-DCNN) 的开发.
- 引入一个扩展的上下文模块 (DCM) 来连接不同的特征地图.
- 整合跨层次的连接,以利用多层次的特征信息进行人群场景分析.
主要成果:
- 拟议的CL-DCNN有效地整合了上下文信息,同时保留了人群场景的本地细节.
- 在五个公共数据集 (ShanghaiTech A/B,Mall,UCF_CC_50,UCF_QNRF) 上的实验显示出卓越的性能.
- 在各自的数据集上实现了52.6,8.1,1.55,181.8和96.4的平均绝对误差 (MAE).
结论:
- 在人群计数方面,CL-DCNN方法显著优于最先进的方法.
- 通过跨层次的上下文信息整合浅层和深层特征是提高性能的关键.
- 拟议的方法为准确的人群密度估计提供了更有效的解决方案.
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