ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Generative adversarial nets and Cerema AWP dataset

5 September 2018, 10:00 – 10:30


Submitted by
SECK Ismaila
Seck Ismaila (Insa Rouen Normandie), Loosli Gaëlle (Université Clermont-Auvergne)
This talk will be about Generative Adversarial Networks(GANs), and a recently introduced dataset, the Cerema AWP(Adverse Weather Pedestrian). We want to assess the capacity of GANs to generate a particular element, in our case a pedestrian, at a specified place. The cerema AWP database is a good database for that task since for each image we have the bounding box of the pedestrian. The Cerema AWP dataset is an image dataset that was produced in a special installation, a tunnel in which different weather condition can be artificially created. And since that database was originally created for pedestrian detection, there is on each image a pedestrian. And the dataset is annotated according to the weather (10 different weathers), the pedestrian (5 different), their clothes (each pedestrian appears with two different clothes). Additional information such as the pedestrian’s direction or the bounding box of the pedestrian is available. The controlled environment, and those detailed information make this database attractive for our purpose. Indeed the background being fixed, it seems to be a simpler version of the problem we would get with different backgrounds, perspectives or other uncontrolled variations. In the cerema AWP database, most of the variation being controlled and associated with labels, we can study the generation, with all the conditions or according to a subset of weather or other conditions. In a previous study using a standard GAN, generated images presented a mixture of weather on a single output, showing that the generative network had trouble matching the dataset distribution. This problem was solved using a conditioning on the weather. Now the generated images have a uniform weather but a problem persists: we don’t have pedestrians on images. We are going to present the architectures, the ways of conditioning and others tricks to help the generator focus on the generation of pedestrians while generating realistic images.
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