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.


Kniaz, V. V., Knyaz, V. A., Hladůvka, J., Kropatsch, W. G., & Mizginov, V. (2019). ThermalGAN: Multimodal Color-to-Thermal Image Translation for Person Re-identification in Multispectral Dataset. In Lecture Notes in Computer Science (pp. 606–624). Springer LNCS. https://doi.org/10.1007/978-3-030-11024-6_46