Title: Crowd counting via scale-adaptive convolutional neural network in extremely dense crowd images

Authors: Ran Yan; Shengrong Gong; Shan Zhong

Addresses: School of Computer Science and Technology, Soochow University, Suzhou 215000, Jiangsu, China ' School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, Jiangsu, China ' School of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, Jiangsu, China

Abstract: Crowd counting, a high accuracy and high-speed technology, has been applied in new retail, shopping mall, underground, rail station and vehicle surveillance systems. However, due to the inconsistent sizes of human heads, there are a lot of counting errors and instability of the crowd density estimation in extremely dense crowd images. Therefore, a scale-adaptive Convolutional Neural Network (CNN) architecture is proposed by introducing residual network on the basis of multi-column CNN. In the process of model training, joint learning is proposed in this paper. Through alternating training for residual network and multi-column CNN, network parameters with the best accuracy are selected after iteration. Joint learning helps to enhance the modelling ability for massive scale transformation and the scale self-adaptability of the network. The proposed method is experimented on public dense crowd data sets. Experimental results prove that scale-adaptive CNN shows higher counting capability than the current state-of-the-art method.

Keywords: crowd counting; density estimation; CNN; convolutional neural network; scale-adaptive; joint learning.

DOI: 10.1504/IJCAT.2019.103298

International Journal of Computer Applications in Technology, 2019 Vol.61 No.4, pp.318 - 324

Received: 06 Jan 2019
Accepted: 07 Mar 2019
Published online: 25 Oct 2019 *

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