Complexity Metric Methodology of Infrared Image Sequence for Single-Object Tracking

The complexity metric of infrared image sequences is crucial to the prediction and evaluation of single-object tracker performance, and it is a research hotspot in the field of computer vision. However, the accuracy and comprehensiveness of the existing complexity metrics are limited. In this paper, an effective method is proposed to quantify the single-object tracking difficulty of infrared image sequences. First, based on the classification and analysis of trackers, the influencing factors of infrared target tracking are summarized. Then, five metrics combining deep features and shallow features are proposed to characterize the complexity of infrared image sequences. Finally, a synthesis complexity metric is designed for comprehensive evaluations of infrared image sequences. Experimental results indicate that our method performs better than traditional methods on comprehensiveness, and the proposed metrics can more accurately reflect the performance of trackers on different infrared image sequences.