一种基于多尺度融合卷积神经网络的人群计数方法

人群间的相互遮挡和多变的空间尺度是基于单幅图像人群计数算法面临的主要挑战.近年来,基于深度学习的人群计数算法在该问题上取得了显著的成效,然而越来越深的网络结构给模型的训练和应用带来了困难.为了解决上述问题,提出了一种基于多尺度融合卷积神经网络(multi-scale fusion convolution neural ...

A crowd counting method based on multi-scale fusion convolutional neural network

Crowd counting has still been a challenge task due to many factors such as mutual occlusion and scale variations. Although the counting algorithms based on deep learning have achieved remarkable successes in recent years on this issue, the deeper and deeper network structure brings difficulties to training and application of the model. To resolve these problems, a novel crowd counting method based on multi-scale fusion convolution neural network (MSFCNN) was proposed in this paper.The MFCNN is composed of three column convolutional neural networks which are utilized to extract multi-scale image features, and these features are fused in a cooperative manner to obtain a crowd density map, and finally the population number is obtained by integrating the density map. The model was evaluated on ShanghaiTech and UCF_CC_50 datasets, and the experimental results demonstrated that the proposed method can adapt to various complex scenes and reduce the impact of occlusion and varied sizes to a large degree. Moreover, the network structure is relatively simple, and thus the model is easy to train, which is superior to some state-of-the-art methods.