A Novel AD-PDA-BACF Algorithm for Real-Time Moving Target Shadow Tracking Using ViSAR Imagery

Abstract

Detection and tracking using video synthetic aperture radar (ViSAR) have attracted a great deal of attention in recent years due to its ability to produce high-resolution videos for regions of interest. In this work, we have chosen the background-aware correlation filter (BACF) as a key algorithm due to its superior performance in real-time tracking scenarios. Dim and small shadows with weak visual features, time-varying shadows, and complex environments pose serious challenges to the target tracking problem using ViSAR videos. These factors lead to a multipeak problem in the BACF and many other correlation filter-based algorithms. As a result, incorrect target detection, template model contamination, and tracker drift or failure during long-term tracking can occur. In this work, we propose a novel appearance-distance information-assisted probabilistic data association (AD-PDA) algorithm to tackle the multipeak problem. Based on the correlation outputs of the BACF, the AD-PDA algorithm selects multiple peak locations as validated measurements. By exploiting the appearance-distance probability distribution functions, the AD-PDA algorithm calculates the mixed appearance-distance association weights and estimates target states accurately. Furthermore, we propose an efficient AD-PDA-BACF algorithm that can track targets accurately by combining the AD-PDA and BACF algorithms. This study conducts experiments using two public ViSAR video datasets released by the Sandia National Laboratory. Our results demonstrate that the proposed algorithm outperforms several state-of-the-art algorithms in tracking dim and small targets in terms of tracking accuracy, success rate, and tracking speed.