Domain Adaptation Through Synthesis for Unsupervised Person Re-identification [chapter]

Sławomir Bąk, Peter Carr, Jean-François Lalonde
2018 Lecture Notes in Computer Science  
Drastic variations in illumination across surveillance cameras make the person re-identification problem extremely challenging. Current large scale re-identification datasets have a significant number of training subjects, but lack diversity in lighting conditions. As a result, a trained model requires fine-tuning to become effective under an unseen illumination condition. To alleviate this problem, we introduce a new synthetic dataset that contains hundreds of illumination conditions.
more » ... lly, we use 100 virtual humans illuminated with multiple HDR environment maps which accurately model realistic indoor and outdoor lighting. To achieve better accuracy in unseen illumination conditions we propose a novel domain adaptation technique that takes advantage of our synthetic data and performs fine-tuning in a completely unsupervised way. Our approach yields significantly higher accuracy than semi-supervised and unsupervised state-of-the-art methods, and is very competitive with supervised techniques.
doi:10.1007/978-3-030-01261-8_12 fatcat:fdpyp2dh7vf2jfrzzwduifbeee