부산대학교 도서관

Benefit from the development of deep learning, recent advances in object tracking have achieved compelling results on the normal sequences. However, in adverse weather, such as haze, it is difficult to extract effective features due to the videos being severely degraded, making existing object tracking methods extremely ineffective. To address this issue, this work introduces an unsupervised domain adaptation framework for natural hazy weather tracking (NHT). Specifically, we employ a Fastvit domain discriminator with a Gradient Reverse Layer (GRL) in the feature extractor to align image features from normal to natural hazy weather. To tackle the significant domain shift between normal and natural hazy weather, we incorporate a synthesized haze dataset as an intermediate domain, achieving progressive domain adaptation. Moreover, we establish a hazy weather dataset namely Haze2023 for training and testing. Comprehensive experimental results demonstrate the generalization ability of our proposed NHT in natural hazy weather.