Fast RGB-T Tracking via Cross-Modal Correlation Filters

Abstract

This paper studies how to perform RGB-T object tracking in the correlation filter framework. Given the input RGB and thermal videos, we utilize the correlation filter for each modality due to its high performance in both of accuracy and speed. To take the interdependency between RGB and thermal modalities, we introduce the low-rank constraint to learn filters collaboratively, based on the observation that different modality features should have similar filters to make them have consistent localization of the target object. For optimization, we design an efficient ADMM (Alternating Direction Method of Multipliers) algorithm to solve the proposed model. Experimental results on the benchmark datasets (i.e., GTOT, RGBT210 and OSU-CT) suggest that the proposed approach performs favorably in both accuracy and efficiency against the state-of-the-art RGB-T methods.