Multi-camera identification and tracking of individual animals is a challenging task in complex monitoring environments due to frequent occlusions, viewpoint changes, and visual similarity between individuals. This challenge is particularly relevant for future animal monitoring scenarios, such as goose tracking in agricultural settings. However, collecting large-scale, well-annotated multi-camera animal datasets remains difficult. In this work, we investigate a conceptual multi-camera tracking and re-identification (ReID) framework based on modern detection, tracking, and appearance-based re-identification methods. The proposed pipeline integrates YOLOv8 for object detection with ByteTrack and BoT-SORT for single-camera multi-object tracking, generating stable tracklets as the basis for cross-camera identity association. To evaluate the feasibility of the re-identification component, experiments are conducted on publicly available human benchmark datasets, which are widely used for appearance-based ReID research. The results demonstrate that the proposed modular pipeline can reliably perform detection, tracking, and identity embedding extraction in multi-camera scenarios. Furthermore, we discuss the transferability of the framework to animal monitoring applications and analyze the limitations arising from domain gaps between human and animal data. The presented study serves as a proof-of-concept and provides a practical foundation for future deployment in real-world goose monitoring scenarios.