TrackEVT: Dual-State Target Maintenance for Embodied Visual Tracking

Embodied visual tracking (EVT) requires an agent to continuously follow a designated moving target from egocentric visual observations. However, high-confidence localization alone cannot confirm that the tracker still follows the designated target. An identity error can steer the agent away and be reinforced by later observations. We refer to this closed-loop failure as target-state takeover. To address it, we propose TrackEVT, which maintains two complementary states. A dense pixel state provides target geometry for visual servoing. In parallel, persistent target and distractor trajectories check whether the propagated pixels still belong to the selected target. When the states disagree, identity trajectories cannot overwrite the control state directly. Instead, identity-constrained rebinding verifies a current-frame target mask before reinitializing pixel propagation. TrackEVT combines pretrained visual components with explicit control and requires no task-specific training. Experiments cover several simulated scenes, target categories, and a real-world deployment. On established benchmarks, TrackEVT achieves the highest mean scores among the compared methods: a mean success rate of 0.97 on Gym-UnrealCV and a mean tracking success rate of 0.98 for aerial vehicle tracking on DAT. A targeted identity stress test further shows that persistent trajectories and verified rebinding improve identity preservation and recovery.