Crowd counting via scale-adaptive convolutional neural network in extremely dense crowd images
by Ran Yan; Shengrong Gong; Shan Zhong
International Journal of Computer Applications in Technology (IJCAT), Vol. 61, No. 4, 2019

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.

Online publication date: Fri, 25-Oct-2019

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