Contributions to Proceedings:

V. Kniaz, V. Knyaz, J. Hladuvka, W. Kropatsch, V. Mizginov:
"ThermalGAN: Multimodal Color-to-Thermal Image Translation for Person Re-identification in Multispectral Dataset";
in: "Computer Vision - ECCV 2018 Workshops", Springer LNCS, 2019, 606 - 624.

English abstract:
We propose a ThermalGAN framework for cross-modality color-thermal person re-identification (ReID). We use a stack of generative adversarial networks (GAN) to translate a single color probe image to a multimodal thermal probe set. We use thermal histograms and feature descriptors as a thermal signature. We collected a large-scale multispectral ThermalWorld dataset for extensive training of our GAN model. In total the dataset includes 20216 color-thermal image pairs, 516 person ID, and ground truth pixel-level object annotations. We made the dataset freely available (http://www.zefirus.org/ThermalGAN/). We evaluate our framework on the ThermalWorld dataset to show that it delivers robust matching that competes and surpasses the state-of-the-art in cross-modality color-thermal ReID.

Person re-identification, Conditional GAN, Thermal images

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.