14 June 2017 Crowd density estimation based on convolutional neural networks with mixed pooling
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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.
© 2017 SPIE and IS&T
Li Zhang, Li Zhang, Hong Zheng, Hong Zheng, Ying Zhang, Ying Zhang, Dongming Zhang, 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 . Submission: Received: 31 August 2016; Accepted: 13 April 2017
Received: 31 August 2016; Accepted: 13 April 2017; Published: 14 June 2017
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