Crowd density estimation based on convolutional neural networks with mixed pooling

Li Zhang; Hong Zheng; Ying Zhang; Dongming Zhang

doi:10.1117/1.JEI.26.5.051403

14 June 2017 Crowd density estimation based on convolutional neural networks with mixed pooling

Li Zhang, Hong Zheng, Ying Zhang, Dongming Zhang

Author Affiliations +

Funded by: Shenzhen Basic Science and Technology Foundation of China

Journal of Electronic Imaging, Vol. 26, Issue 5, 051403 (June 2017). https://doi.org/10.1117/1.JEI.26.5.051403

Abstract

Crowd density estimation is an important topic in the fields of machine learning and video surveillance. Existing methods do not provide satisfactory classification accuracy; moreover, they have difficulty in adapting to complex scenes. Therefore, we propose a method based on convolutional neural networks (CNNs). The proposed method improves performance of crowd density estimation in two key ways. First, we propose a feature pooling method named mixed pooling to regularize the CNNs. It replaces deterministic pooling operations with a parameter that, by studying the algorithm, could combine the conventional max pooling with average pooling methods. Second, we present a classification strategy, in which an image is divided into two cells and respectively categorized. The proposed approach was evaluated on three datasets: two ground truth image sequences and the University of California, San Diego, anomaly detection dataset. The results demonstrate that the proposed approach performs more effectively and easily than other methods.

Citation Download Citation

Li Zhang, Hong Zheng, Ying Zhang, and Dongming Zhang "Crowd density estimation based on convolutional neural networks with mixed pooling," Journal of Electronic Imaging 26(5), 051403 (14 June 2017). https://doi.org/10.1117/1.JEI.26.5.051403

Received: 31 August 2016; Accepted: 13 April 2017; Published: 14 June 2017

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