摘要
在计算机视觉领域,针对小角度俯拍下的站台人群计数的研究工作较少,且计数精度普遍较低。人群计数算法往往通过图像分割识别出图片中的所有行人个体,并进行数量统计,具有很重要的现实意义。然而现有的图像分割算法往往只能适用于简单场景下的简单分割任务。由于小角度俯拍下的站台场景中存在行人近大远小、行人互相遮挡和行人轮廓姿态多样等原因,因此给有效分割计数带来了较大的挑战。针对这一任务,提出了距离自适应卷积神经网络(Distance Adaptive Convolutional Neutral Network,简称DACNN),通过改进回归对象和设计距离自适应卷积层,成功实现了对小角度俯拍下站台人群的准确计数。经过一系列实验表明,该模型不仅计数精度高,而且计数速度较快、鲁棒性良好,具有广阔的运用前景。
In the field of computer vision,there are few research work on platform crowd counting under small angles and the counting accuracy is generally low. Crowd counting algorithms often identify all the individuals in the image through image segmentation,and make the counting of the crowd,which is of great practical significance. However,the existing image segmentation algorithms are often only applicable to simple segmentation tasks in simple scenes. Since the human body caused by small angles in the vicinity looks larger while in the distance seems small,the body block each other and have diverse stance,to effectively split the statistics becomes a great challenge. In response to this task,this paper proposes Distance Adaptive Convolutional Neutral Network(DACNN). By designing a distance-adaptive convolutional layer and improving the model loss function,we can achieve a better performance under small angles of platform crowd counting. After a series of experiments show that the algorithm model not only has a high counting accuracy and fast counting speed,but also has good practicality,can be widely used.
作者
魏文戈
陈舒荻
WEI Wen-ge;CHEN Shu-di(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing,Jiangsu 211106,China)
出处
《计算技术与自动化》
2019年第4期117-120,共4页
Computing Technology and Automation
关键词
小角度俯拍
站台人群计数
图像分割
距离自适应
small angle
platform crowd counting
image segmentation
distance-adaptive
