Paper Info
Title
Improved Crowd Counting Method Based on Scale-Adaptive Convolutional Neural Network.
Abstract
Crowd counting is a challenging task due to the influence of various factors, such as scene transformation, complex crowd distribution, uneven illumination, and occlusion. To overcome such problems, scale-adaptive convolutional neural network (SaCNN) used a convolutional neural network to obtain high-quality crowd density map estimation and integrate the density map to get the estimated headcount. To obtain better performance on crowd counting, an improved crowd counting method based on SaCNN was proposed in this paper. The spread parameter, i.e., the standard variance, of geometry-adaptive Gaussian kernel used in SaCNN was optimized to generate a higher quality ground truth density map for training. The absolute count loss with weight 4e-5 was used to jointly optimize with the density map loss to improve the network generalization ability for crowd scenes with few pedestrians. Also, a random cropping method was applied to improve the diversity of training samples to enhance network generalization ability. The experimental results upon ShanghaiTech public dataset showed that the proposed method can obtain more accurate and more robust results on crowd counting than those of SaCNN.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2899939
IEEE ACCESS
Keywords
Field
DocType
Crowd counting,convolutional neural network,scale-adaptive convolutional neural network (SaCNN),density map,scale-adaptive,absolute count loss
Kernel (linear algebra),Task analysis,Pattern recognition,Convolutional neural network,Computer science,Ground truth,Crowd density,Artificial intelligence,Crowd counting,Gaussian function,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
1
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Jun Sang14012.62
Weiqun Wu221.04
Hongling Luo310.34
Hong Xiang4265.56
Qian Zhang529043.11
Haibo Hu632.44
Xiaofeng Xia711.36