【摘要】 估算监控场景中的人数是安防监控的重要任务之一,当人群密集、行人之间存在遮挡时,人数估计较困难。因此,针对密集场景下的人数估计问题,提出了一种改进的基于卷积神经网络的人数估计方法。为了改善摄像透视畸变带来的影响,分别利用深层网络和浅层网络提取人群特征,深层和浅层网络分别设计了不同核大小的卷积层,并将提取到的特征通过一个具备多尺度提取能力的结构进行融合。实验结果表明,改进后的网络模型所获取的人群密度图更加贴近原场景信息,人数估计结果也更加精确。更多还原

【Abstract】 Estimating the number of people in the surveillance scene is one of the important tasks of security monitoring.However it is difficult to estimate the number when the crowd is with clutter and severe occlusion.An improved crowd counting method based on the convolution neural network is proposed as for the number estimation under dense scenes.In order to reduce the effect of camera perspective distortion,the deep network and shallow network are used to extract the crowd characteristics,respectively.The convolution layers with different kernel sizes are also designed.Moreover,the extracted features are fused through a special structure with multi-scale extraction capability.The experimental results show that the crowd density map obtained by the improved network model is closer to the original scene information and the obtained prediction results are more precise.更多还原