DAnet:Depth-Aware Network For Crowd Counting

Van Su Huynh, Vu Hoang Tran, Ching Chun Huang

研究成果: Conference contribution

摘要

Image-based people counting is a challenging work due to the large scale variation problem caused by the diversity of distance between the camera and the person, especially in the congested scenes. To handle this problem, the previous methods focus on building complicated models and rely on labeling the sophisticated density maps to learn the scale variation implicitly. It is often time-consuming in data pre-processing and difficult to train these deep models due to the lack of training data. In this paper, we thus propose an alternative and novel way for crowd counting which handles the scale variation problem by leveraging the auxiliary depth estimation dataset. Using separated crowd and depth datasets, we train a unified network for two tasks- crowd density map estimation and depth estimation- at the same time. By introducing the auxiliary depth estimation task, we prove that the scale problem caused by distance can be well solved and the labeling cost can be reduced. The efficacy of our method is demonstrated in the extensive experiments by multiple evaluation criteria.
原文English
主出版物標題2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
發行者IEEE Computer Society
頁面3001-3005
頁數5
ISBN(電子)9781538662496
DOIs
出版狀態Published - 九月 2019
事件26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan
持續時間: 22 九月 201925 九月 2019

出版系列

名字Proceedings - International Conference on Image Processing, ICIP
2019-September
ISSN(列印)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
國家Taiwan
城市Taipei
期間22/09/1925/09/19

Keywords

  • Estimation , Training , Task analysis , Decoding , Feature extraction , Cameras , Training data

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    Huynh, V. S., Tran, V. H., & Huang, C. C. (2019). DAnet:Depth-Aware Network For Crowd Counting. 於 2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings (頁 3001-3005). [8804413] (Proceedings - International Conference on Image Processing, ICIP; 卷 2019-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2019.8804413