Fine-Grained Feature and Template Reconstruction for TIR Object Tracking

Abstract

Thermal infrared (TIR) object tracking is a significant subject within the field of computer vision. Currently, TIR object tracking faces challenges such as insufficient representation of object texture information and underutilization of temporal information, which severely affects the tracking accuracy of TIR tracking methods. To address these issues, we propose a TIR object tracking method (called: FFTR) based on fine-grained feature and template reconstruction. Specifically, aiming at the fine-grained information of the TIR object, we employ a frequency channel attention mechanism that transforms TIR images into the frequency domain using discrete cosine transform features. By capturing the fine-grained feature of TIR images from the frequency domain, we enhance the model’s ability to comprehend these images. To better leverage temporal information, we utilize a template region reconstruction method. This method reconstructs the template from the previous frame based on the search area of the current frame, which is then incorporated into the attention computation for the subsequent frame, thereby improving the tracking capability of TIR objects. Extensive quantitative and qualitative experiments show that our method achieves competitive tracking performance on the TIR benchmarks.