Scene-specific crowd counting using synthetic training images

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.

Automated summary

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.