Correlation Filters Based on Strong Spatio-Temporal for Robust RGB-T Tracking

Abstract

In this paper, we propose a strong spatio-temporal mechanism with correlation filters to solve multi-modality tracking tasks. First, we use the features of the previous four frames as spatio-temporal features, then aggregate the spatio-temporal features into the filters learning and positioning of the adjacent frame. Second, we enhance the temporal and spatial characteristics of the current frame filter by learning the previous four frame filters and spatial penalty. From the experimental results on the GTOT, VOT-TIR2019 and RGBT234 datasets, our strong spatio-temporal correlation filters has achieved excellent performance.