Special Section on Video Analytics for Public Safety

Crowd density estimation based on convolutional neural networks with mixed pooling

[+] Author Affiliations
Li Zhang, Ying Zhang, Dongming Zhang

Wuhan University, School of Electronic Information, Wuhan, Hubei, China

Hong Zheng

Wuhan University, School of Electronic Information, Wuhan, Hubei, China

Shenzhen Institute of Wuhan University, Shenzhen, Guangdong, China

J. Electron. Imaging. 26(5), 051403 (Jun 14, 2017). doi:10.1117/1.JEI.26.5.051403
History: Received August 31, 2016; Accepted April 13, 2017
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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.

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Citation

Li Zhang ; Hong Zheng ; Ying Zhang and Dongming Zhang
"Crowd density estimation based on convolutional neural networks with mixed pooling", J. Electron. Imaging. 26(5), 051403 (Jun 14, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.5.051403


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