Domain Adversarial Training for Infrared-colour Person Re-Identification
This addresses a practical need for cross-modal person re-identification in video surveillance, but is incremental as it builds on existing domain adversarial methods.
The paper tackles the problem of matching persons between infrared and color images in poorly-lit surveillance environments by proposing a part-feature extraction network and a novel domain adversarial training variant, achieving state-of-the-art performance.
Person re-identification (re-ID) is a very active area of research in computer vision, due to the role it plays in video surveillance. Currently, most methods only address the task of matching between colour images. However, in poorly-lit environments CCTV cameras switch to infrared imaging, hence developing a system which can correctly perform matching between infrared and colour images is a necessity. In this paper, we propose a part-feature extraction network to better focus on subtle, unique signatures on the person which are visible across both infrared and colour modalities. To train the model we propose a novel variant of the domain adversarial feature-learning framework. Through extensive experimentation, we show that our approach outperforms state-of-the-art methods.