Probability-Driven Gating for Resilient Multi-Modal Tracking in Robotic Systems

Abstract

The deployment of robots in unstructured environments demands perception systems that are both accurate and resilient. While RGB-Thermal (RGB-T) fusion is promising, current trackers often fail due to rigid, non-adaptive fusion strategies and underutilized cross-modal cues, compromising reliability for robotics. We introduce DSTrack, a novel tracking framework that embeds two core mechanisms for robotic robustness: a Probability-Gated Dynamic Switch and a Synergistic Multi-Domain Enhancement Network. The switch acts as an online decision-maker, allowing the robot to dynamically select the most reliable fusion path based on real-time confidence estimation, enabling crucial adaptation to scene changes. The enhancement network concurrently strengthens target representations within each modality through tri-domain (channel, spatial, frequency) refinement and establishes compensatory links between modalities via a cross-attention module, ensuring performance even during partial sensor degradation. Extensive evaluations on RGB-T benchmarks demonstrate state-of-the-art accuracy. More critically, DSTrack exhibits key properties for robotic integration: real-time environmental adaptability, inherent sensor fault tolerance, and consistent output for downstream planning.