Context-aware MDNet for target tracking in UAV remote sensing videos

Abstract

ABSTRACTWith the maturity and popularity of unmanned aerial vehicle (UAV) technology, remote sensing target tracking in the aerial videos from UAVs has drawn much attention in recent years. Given that UAV aerial video has low resolution, multiple similar disruptors and rapid perspective changes, and the main tracking methods in this research field generally have low tracking performance and timeliness, we propose a remote sensing target tracking method for UAV aerial video based on a context-aware multi-domain convolutional neural network (CAMD). First, in the design of the tracking network structure, we fuse multiple convolutional layers using residual connections to boost the effectiveness of regression learning. Then, in the offline pretraining stage, a Rotation Adversarial Autoencoders (RAAE) was adopted to generate typical easily confused negative samples for enhancing the capacity to distinguish between targets and the background interference. Finally, when the estimated target score was in the ‘fuzzy interval’, we introduced a response-adaptive context-aware correlation filter (RA-CACF) module into our network architecture to improve the tracking performance. Qualitative and quantitative evaluations using public and homemade hard datasets demonstrate that the proposed method can achieve high accuracy and efficiency results compared to state-of-the-art trackers.