Pedestrian Re-Identification and Tracking Algorithm Based on Cross-Domain Adaptation

With the increasing demand for intelligent surveillance and public safety, Apedestrian re- identification and tracking technology has become a focal point in the field of computer vision. Traditional algorithms for pedestrian re-identification and tracking exhibit significant performance degradation when applied to cross-domain scenarios, such as those involving different surveillance devices or varying lighting conditions. Although existing studies have made some progress through the use of deep learning techniques, challenges remain in enhancing cross-domain adaptability. To address this issue, this study proposes a pedestrian re-identification image keypoint detection method based on adversarial generative domain adaptation networks, as well as a pedestrian re-identification and tracking algorithm based on deep self-supervised adversarial domain adaptation networks. By combining generative adversarial networks (GANs) with self-supervised learning, the proposed method significantly improves the accuracy and robustness of pedestrian re- identification and tracking in complex cross-domain environments, demonstrating high practical value and applicability.