Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 5046727, 11 pages
https://doi.org/10.1155/2017/5046727
Research Article

Low-Rank and Sparse Based Deep-Fusion Convolutional Neural Network for Crowd Counting

PLA University of Science and Technology, Nanjing, Jiangsu, China

Correspondence should be addressed to Zhisong Pan; moc.liamtoh@szptoh

Received 14 March 2017; Revised 10 July 2017; Accepted 25 July 2017; Published 25 September 2017

Academic Editor: Suzanne M. Shontz

Copyright © 2017 Siqi Tang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes an accurate crowd counting method based on convolutional neural network and low-rank and sparse structure. To this end, we firstly propose an effective deep-fusion convolutional neural network to promote the density map regression accuracy. Furthermore, we figure out that most of the existing CNN based crowd counting methods obtain overall counting by direct integral of estimated density map, which limits the accuracy of counting. Instead of direct integral, we adopt a regression method based on low-rank and sparse penalty to promote accuracy of the projection from density map to global counting. Experiments demonstrate the importance of such regression process on promoting the crowd counting performance. The proposed low-rank and sparse based deep-fusion convolutional neural network (LFCNN) outperforms existing crowd counting methods and achieves the state-of-the-art performance.