期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 38, 期 -, 页码 530-539出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2016.03.021
关键词
Crowd counting; Convolutional neural networks; Feature learning; Regression
资金
- National Natural Science Foundation (NSF) of China [61300056, 61572029]
- Anhui Provincial Natural Science Foundation of China [1408085QF118]
- Science and Technology Project of Anhui Province [1501b042207]
For reasons of public security, modeling large crowd distributions for counting or density estimation has attracted significant research interests in recent years. Existing crowd counting algorithms rely on predefined features and regression to estimate the crowd size. However, most of them are constrained by such limitations: (1) they can handle crowds with a few tens individuals, but for crowds of hundreds or thousands, they can only be used to estimate the crowd density rather than the crowd count; (2) they usually rely on temporal sequence in crowd videos which is not applicable to still images. Addressing these problems, in this paper, we investigate the use of a deep-learning approach to estimate the number of individuals presented in a mid-level or high-level crowd visible in a single image. Firstly, a ConvNet structure is used to extract crowd features. Then two supervisory signals, i.e., crowd count and crowd density, are employed to learn crowd features and estimate the specific counting. We test our approach on a dataset containing 107 crowd images with 45,000 annotated humans inside, and each with head counts ranging from 58 to 2201. The efficacy of the proposed approach is demonstrated in extensive experiments by quantifying the counting performance through multiple evaluation criteria. (C) 2016 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据