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

Siqi Tang,Zhisong Pan,Xingyu Zhou

doi:10.1155/2017/5046727

Siqi Tang, Zhisong Pan + Show 1 more

Open Access

https://doi.org/10.1155/2017/5046727

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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.

Highlights

Recent years have witnessed extensively crowded scenes, such as concerts, political speeches, ceremonies, marathons, and tourist spots
This paper is not the first one to leverage convolutional neural network (CNN) model to promote the accuracy of crowd counting, whereas most of the CNN based crowd counting methods adopt a two-stage pipeline: crowd density estimation with an end-to-end deep network and direct integral to obtain the global counting, which accumulates the errors and limits the promotion of counting accuracy
To solve this problem and promote the accuracy of crowd counting, we propose a low-rank and sparse based deep-fusion convolutional neural network (LFCNN), which adopts the low-rank and sparse penalty based regression process instead of the direct integral

Summary

Introduction

Recent years have witnessed extensively crowded scenes, such as concerts, political speeches, ceremonies, marathons, and tourist spots. This paper is not the first one to leverage convolutional neural network (CNN) model to promote the accuracy of crowd counting, whereas most of the CNN based crowd counting methods adopt a two-stage pipeline: crowd density estimation with an end-to-end deep network and direct integral to obtain the global counting, which accumulates the errors and limits the promotion of counting accuracy To solve this problem and promote the accuracy of crowd counting, we propose a low-rank and sparse based deep-fusion convolutional neural network (LFCNN), which adopts the low-rank and sparse penalty based regression process instead of the direct integral. Aiming to improve the accuracy of global counting regression, which is the ultimate objective of crowd counting methods, we adopt least squares regression with low-rank and sparse penalty to project the estimated density map to global counting, instead of the direct integral process adopted by most existing CNN based crowd counting methods. Experiments on large-scale crowd counting datasets demonstrate that, to our knowledge, LFCNN can outperform other methods and achieve the state-of-the-art performance in crowd counting application

Related Works

Notation and Problem Definition

Deep-Fusion Density Map Regression Network

Low-Rank and Sparse Based Regression

Experiments

Conclusion