Journal
PATTERN RECOGNITION
Volume 124, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2021.108484
Keywords
Crowd counting; Scene-specific settings; Synthetic training images
Funding
- project Law Enforcement agencies human factor methods and Toolkit for the Security and protection of CROWDs in mass gatherings (LETSCROWD), EU Horizon 2020 programme [740466]
- project IMaging MAnagement Guidelines and Informatics Network for law enforcement Agencies (IMMAGINA) , European Space Agency, ARTES Integrated Applications Promotion Programme [4000133110/20/NL/AF]
- H2020 Societal Challenges Programme [740466] Funding Source: H2020 Societal Challenges Programme
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This study proposes a method for automatically generating a synthetic image training set for crowd counting in application scenarios where representative images are lacking. Experimental results show that this method can improve the effectiveness of existing crowd counting methods.
Crowd counting is a computer vision task on which considerable progress has recently been made thanks to convolutional neural networks. However, it remains a challenging task even in scene-specific settings, in real-world application scenarios where no representative images of the target scene are available, not even unlabelled, for training or fine-tuning a crowd counting model. Inspired by previous work in other computer vision tasks, we propose a simple but effective solution for the above application scenario, which consists of automatically building a scene-specific training set of synthetic images. Our solution does not require from end-users any manual annotation effort nor the collection of representative images of the target scene. Extensive experiments on several benchmark data sets show that the proposed solution can improve the effectiveness of existing crowd counting methods. (C) 2021 Elsevier Ltd. All rights reserved.
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