Object Re Identification

Object re-identification (ReID) focuses on reliably identifying the same object across different images, even with variations in viewpoint, lighting, or object condition. Current research emphasizes robust feature representation learning, often employing transformer-based architectures and hierarchical graph networks to capture both global and local object features, addressing challenges like scale variations and occlusions. This work is driven by the need for improved performance in diverse applications, including robotics, surveillance, and intelligent monitoring systems, where accurate object tracking and identification are crucial. The development of large-scale datasets, including those incorporating damaged or deformed objects and aerial imagery, is also a significant area of focus.