A Confidence-Based Sampling Strategy for Dense Temporal Token Learning in Thermal Infrared Object Tracking

Single object tracking (SOT) aims to locate and follow a target object in a video sequence. While most existing SOT methods rely on color images due to the widespread use of color cameras, challenging weather (e.g., nighttime, rain, fog) and rapid object pose variations often cause lost or missed tracks, degrading performance. These issues can be miti-gated by utilizing thermal infrared (TIR) data, which is less susceptible to adverse weather conditions, and leveraging temporal information to handle the significant object pose changes across frames. This paper introduces a confidence-based sampling strategy for dense temporal token SOT, using the TIR data to address tracking failures. Our approach generates both the object’s state, as in conventional methods, and an additional confidence score that quantifies the tracking reliability. Obtained from a simple yet effective multi-layer perceptron-based scoring head, which processes dense temporal tokens capturing the target’s appearance, spatio-temporal location, and trajectory, the confidence score guides the sampling strategy to suggest optimal reference frames for subsequent tracking. Intensive experiments on LSOTB-TIR, the largest TIR dataset, demonstrate that our proposed method achieves state-of-the-art performance, improving upon the baseline by 1.17% in success rate, 1.50% in precision, and 1.07% in normalized precision.