MIRNet: A Robust RGBT Tracking Jointly with Multi-Modal Interaction and Refinement
Ruichao Hou, Tongwei Ren, Gangshan Wu
Abstract
RGBT tracking attempts to design a robust all-weather tracker by integrating the complementary features of visible and thermal spectrums. To explore the latent interdependencies across modalities, we propose a novel real-time tracker named MIR-Net, which contains a multi-modal interaction module (MIM) and a refinement mechanism (RM), thereby adaptively merging multi-modal features and achieving precise scale estimation. Specifically, to enhance instance representation in low-quality modality, the MIM reinforces discriminative features from one modality to another in a bidirectional way. Considering the negative effects of unreliable modality, we further introduce a gate function in MIM to filter redundancy. To address the problem of random drifting and estimate the precise scale in the online tracking, we present a well-designed RM that combines optical flow and refinement network. Comprehensive experiments on two public RGBT benchmarks validate that our tracker outperforms the state-of-the-art methods.