Cross-scene crowd counting via deep convolutional neural networks
Refereed conference paper presented and published in conference proceedings

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AbstractCross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to finetune the trained CNN model for the target scene. A new dataset including 108 crowd scenes with nearly 200,000 head annotations is introduced to better evaluate the accuracy of cross-scene crowd counting methods. Extensive experiments on the proposed and another two existing datasets demonstrate the effectiveness and reliability of our approach.
All Author(s) ListZhang C., Li H., Wang X., Yang X.
Name of ConferenceIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Start Date of Conference07/06/2015
End Date of Conference12/06/2015
Place of ConferenceBoston
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by IEEE,
Volume Number07-12-June-2015
Pages833 - 841
LanguagesEnglish-United Kingdom

Last updated on 2021-12-09 at 23:46