Remote Sensing Target Tracking in UAV Aerial Video Based on Saliency Enhanced MDnet

Remote sensing target tracking in the aerial video from unmanned aerial vehicles (UAV) plays a key role in public security. As the UAV aerial video has rapid changes in scale and perspective, few pixels in the target region, and multiple similar disruptors, and the main tracking methods in this research field generally have relatively low tracking performance and timeliness, we propose a remote sensing target tracking method for the UAV aerial video based on a saliency enhanced multi-domain convolutional neural network (SEMD). First, in the pre-training stage, we combine the least squares generative adversarial networks (LSGANs) with a multi-orientation Gaussian Pyramid to augment typical easily confused negative samples for enhancing the capacity to distinguish between targets and the background. Then, a saliency module was integrated into our tracking network architecture to boost the saliency of the feature map, which can improve the representation power of a rapid dynamic change target. Finally, in the stage for generating tracking samples, we implemented a local weight allocation model to screen for hard negative samples. This approach can not only improve the stability in tracking but also boost efficiency. The comprehensive evaluations of public and homemade hard datasets demonstrate that the proposed method can achieve high accuracy and efficiency results compared with state-of-the-art methods.