Using synthetic images has been proposed to avoid collecting and manually annotating a sufficiently large and representative training set for several computer vision tasks, including crowd counting. While existing methods for crowd counting are based on generating realistic images, we start investigating how crowd counting accuracy is affected by increasing the realism of synthetic training images. Preliminary experiments on state-of-the-art CNN-based methods, focused on image background and pedestrian appearance, show that realism in both of them is beneficial to a different extent, depending on the kind of model (regression- or detection-based) and on pedestrian size in the images.

How Realistic Should Synthetic Images Be for Training Crowd Counting Models?

Ledda E.;Putzu L.;Delussu R.;Loddo A.;Fumera G.
2021-01-01

Abstract

Using synthetic images has been proposed to avoid collecting and manually annotating a sufficiently large and representative training set for several computer vision tasks, including crowd counting. While existing methods for crowd counting are based on generating realistic images, we start investigating how crowd counting accuracy is affected by increasing the realism of synthetic training images. Preliminary experiments on state-of-the-art CNN-based methods, focused on image background and pedestrian appearance, show that realism in both of them is beneficial to a different extent, depending on the kind of model (regression- or detection-based) and on pedestrian size in the images.
978-3-030-89130-5
978-3-030-89131-2
Crowd counting; Synthetic training images
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/341552
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