Unified Tracking Model

Unified tracking models aim to consolidate various object tracking tasks—including single object tracking, multi-object tracking, and video object segmentation—into a single, versatile framework. Current research emphasizes developing models that handle diverse modalities (e.g., RGB, depth, language) and different reference types (bounding boxes, masks, natural language descriptions) using techniques like contrastive learning, transformer architectures, and tracking-with-detection paradigms. This unification improves efficiency by sharing model parameters and data across tasks, leading to more robust and generalizable tracking performance with potential applications in autonomous driving, surveillance, and robotics.