自动化学报2016，Vol.42Issue(6)：866-874,9.DOI:10.16383/j.aas.2016.c150663
基于序的空间金字塔池化网络的人群计数方法
Crowd Counting Using Rank-based Spatial Pyramid Pooling Network
摘要
Abstract
Crowd counting in videos has an important value in the field of intelligent surveillance. Due to the constraints resulting from camera perspective, uneven distribution of crowd density, background clutter, and occlusions, traditional low-level features-based methods suffer from low counting accuracy. In this paper, a new crowd counting method is proposed based on rank-based spatial pyramid pooling (RSPP) network. In the proposed method, the original image is divided into several sub-regions with the same scope of perspective, and then multi-scale sub-image blocks are respectively taken from different sub-regions. Rank-based spatial pyramid pooling network is used to get the numbers of pedestrians in sub-image blocks. Then summing the numbers of persons of all sub-image blocks gives the total number of people on the image. The proposed image blocking method eliminates the effect of camera perspective and uneven distribution of crowd density on crowd counting. The proposed rank-based spatial pyramid pooling can not only handle multi-scale sub-image blocks, but also solve the problem of huge important information loss and over-fitting encountered by traditional pooling methods. Experimental results show that the proposed method has the advantages of high accuracy and good robustness compared with traditional methods.关键词
人群计数/空间金字塔池化/深度学习/卷积神经网络/岭回归Key words
Crowd counting/spatial pyramid pooling (SPP)/deep learning (DL)/convolutional neural network (CNN)/ridge regression引用本文复制引用
时增林,叶阳东,吴云鹏,娄铮铮..基于序的空间金字塔池化网络的人群计数方法[J].自动化学报,2016,42(6):866-874,9.基金项目
国家自然科学基金(61170223,61502432,61502434)资助Supported by National Natural Science Foundation of China (61170223,61502432,61502434) （61170223,61502432,61502434）
