Scene-specific crowd counting using synthetic training images

Highlights：

• We propose a method for learning scene-specific crowd counting models when neither labelled nor representative unlabelled images of the target scene are available.

• Our method consists of generating synthetic images with the same background, perspective and scale as the target scene.

• Our method can be applied to any regression-based crowd counting method.

• Experiments on benchmark data sets show that our method can outperform the alternative cross-scene approach for both state-of-the-art CNN-based methods and early regression-based ones.