A Re-detection Target Tracking Framework after Loss for Unmanned Aerial Vehicle

Abstract

Currently, the target tracking algorithm based on discriminative correlation filter (DCF) and deep learning (DL) plays an increasingly important role in UAV (unmanned aerial vehicle). However, existing algorithms have limitations such as limited search region, difficulty in re-capturing targets when tracking is lost, and high computational complexity, which makes them difficult to apply to the UAV platform. In this paper, a target re-detection tracking algorithm (RDT) after tracking loss is designed for mobile platforms. RDT uses DCF in simple scenes to track targets and establish a target model between different frames. To ensure the algorithm when tracking is lost due to occlusion or moving out of view, an efficient switching criterion is designed to indirectly invoke the detection algorithm to re-capture the target and guide online learning of DCF. And the experiment results on the OTB benchmark show that RDT can re-capture the target after tracking is lost, and the speed of operation on the CPU is 85fps.