基于光照自适应动态一致性的无人机目标跟踪

无人机跟踪任务经常面临各种光线变化场景，然而无人机跟踪方法主要在光线充足下实现鲁棒跟踪。提出一种具有光照自适应性和跨帧语义感知动态一致性评估的无人机跟踪方法，实现光线不足下的无人机目标跟踪。首先构建光照自适应模块对昏暗场景进行识别，对视频图像的光照强度进行补偿；其次构建目标模板训练具有目标感知能力的滤波器进行相关运算，并利用跨帧之间的响应信息进行一致性评估；最后构建动态约束策略并对响应差异进行约束，使跟踪器保持时间平滑。在UAVDark135和UAV123数据集上，与9种先进算法进行对比实验，结果表明该算法具有较好的跟踪性能。

Unmanned aerial vehicles object tracking based on illumination adaptive dynamic consistency

The tracking of unmanned aerial vehicles (UAVs) is often confronted with illumination change scenes. However, UAV tracking methods mainly achieve robust tracking under sufficient illumination. In this study, a UAV tracking method that uses dynamic consistency evaluation based on illumination adaptability and cross-frame semantic perception is proposed to realize efficient UAV object tracking under insufficient illumination. First, an adaptive illumination module was designed to recognize dim scenes and compensate for the illumination intensity of the dim image. Subsequently, aobject template was built to train a filter with object perception to perform correlation operations; furthermore, the consistency evaluation was performed using the response information between the cross-frames. Finally, a dynamic constraint strategy was developed, and the response difference of the tracker was constrained to maintain temporal smoothness. When the tracking performance of this method was compared to that of nine state-of-the-art algorithms on the UAVDark135 and UAV123 datasets, the results indicated that the algorithm had better tracking performance.