Video crowd counting method based on conv-pooling deep spatial and temporal features

Qiang LI; Zilu KANG

doi:10.11959/j.issn.1000-0801.2018161

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Video crowd counting method based on conv-pooling deep spatial and temporal features

research and development | 更新时间：2024-06-05

- Video crowd counting method based on conv-pooling deep spatial and temporal features
- Telecommunications Science Vol. 34, Issue 6, Pages: 72-79(2018)
- 作者机构：
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- DOI：10.11959/j.issn.1000-0801.2018161
  CLC： TP391
- Published Online：2018-06，
  
  Published：20 June 2018
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Qiang LI, Zilu KANG. Video crowd counting method based on conv-pooling deep spatial and temporal features[J]. Telecommunications science, 2018, 34(6): 72-79.
DOI：

Qiang LI, Zilu KANG. Video crowd counting method based on conv-pooling deep spatial and temporal features[J]. Telecommunications science, 2018, 34(6): 72-79. DOI： 10.11959/j.issn.1000-0801.2018161.

摘要

由于摄像机角度、背景、人群密度分布和遮挡的限制，传统的基于底层视觉特征的视频人群计数方法往往难以实现理想的效果。利用视频的时空特征和卷积—池化方法形成高层的视觉特征，采用局部特征聚合描述符进行量化和码本计算，实现了对视频人群信息的精准描述；该方法充分利用了视频的运动和外观信息，基于卷积神经网络和池化方法提升了对视频本征属性和特征的描述能力。实验结果表明，所提方法比传统的视频人群计数方法具有更高的精度和更好的顽健性。

Abstract

Due to angle of camera

background

population density distribution and occlusion limitations

traditional video crowd counting methods based on underlying visual features are often difficult to achieve ideal results.Using the temporal and spatial features of video and conv-pooling method

high-level visual features were formed

local feature aggregation descriptors were used for quantization and codebook calculation to achieve accurate description of video crowd information.This method made full use of video motion and appearance information.Based on convolutional neural networks and pooling methods

the ability to describe video intrinsic attributes and features was improved.Experimental results show that the proposed method has higher precision and better robustness than traditional video crowd counting methods.

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references

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