Abstract:
Person re-identification (ReID) is a fundamental computer vision task with numerous
real-world applications, such as surveillance and robotics, enabling reliable human
identification across several surveillance camera feeds in the fast-developing environment
of computer vision. However, the performance of ReID models often degrades
significantly when deployed in new camera domains due to the domain shift problem.
To address this challenge, this thesis presents the Unified and Elevated Learning
(UEL) framework for unsupervised cross-domain person ReID. that tackles the
critical issue of domain shift. Domain shift occurs when a ReID model trained on
one camera domain experiences a significant drop in performance when deployed in a
new, visually distinct camera domain. This problem hinders the practical application
of ReID systems in real-world scenarios. The UEL framework leverages a three-stage
training approach that includes GAN-based camera-style data augmentation, source
domain pre-training, and end-to-end cooperative learning. The key innovations of the
UEL framework include adversarial training to extract camera-invariant features, cooperative
learning to reduce noise in pseudo-labels, and dynamic fine-tuning of the
data augmentation ratio. Extensive experiments on widely used benchmarks, such as
Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the superiority of the
UEL framework over state-of-the-art unsupervised domain adaptation methods for
person ReID. The ablation study further highlights the crucial role of the adversarial
training component in the overall performance. The adaptability of the Unified and
Elevated Learning framework to different backbone models is also validated. These
results showcase the potential of the UEL framework for practical person ReID applications.
The thesis contributes to the development of robust and adaptable person
ReID systems that can operate effectively in real-world scenarios.
