无人机对地目标跟踪的快速初始化和自适应优化

Bounding box initialization and optimization for ground target tracking in UAV Videos

目标跟踪算法的性能通常和初始跟踪框的质量有关。在无人机对地侦察任务中,由于反应时间有限,操作员通常难以选取精确的初始跟踪框,导致目标跟踪结果较差。针对这一问题,提出一种半自动的跟踪框快速初始化和自适应优化策略,并给出基于视觉显著性和显著图像分割的自适应优化算法样例,在性能提升和运行时间上均具有优势。与优化前相比,在2个数据集上的跟踪成功率最高提升0.262、跟踪精度最高提升0.177;在运行时间方面,处理200像素×200像素的图像切片时,理论并行速度可达10帧/s。提出的跟踪框初始化和优化策略,结合了人的主观选择和视觉认知,可以有效解决无人机对地侦察任务中目标难以锁定的问题,并具备在嵌入式设备中的可移植性。

The performance of object tracking algorithm is usually related to the quality of initial bounding box.In UAV(unmannd aerial vehicle)ground reconnaissance tasks,due to limited response time,it is often difficult for operators to draw accurate initial bounding box,resulting in poor object tracking results.Current bounding box initialization method has some limitations and cannot meet the needs of UAV ground reconnaissance tasks.To meet the demands of actual system,a semi-automatic initialization and optimization strategy was proposed in combination with human subjective choice and visual cognition,which could give example of adaptive optimization algorithm based on visual saliency and salient region segmentation.The strategy was divided into 3 stages:coarse election,adaptive optimization and fine selection.The effectiveness of tracking box optimization algorithm was verified on 2 benchmark datasets.On VisDrone2018-SOT-test-dev dataset,in comparison with before optimization,the average success rate is increased by 0.138,and the highest is increased by 0.262.The average accuracy is increased by 0.135,and the highest is increased by 0.165.On UAVDT(unmanned aerial vehicle detection and tracking)dataset,in comparison with before optimization,the average success rate is increased by 0.093,and the highest is increased by 0.147.The average accuracy is increased by 0.082,and the highest is increased by 0.177.When processing 200×200 pixels image slices,theoretical parallel speed can reach 10 frame/s,which basically meets the real-time requirements.The proposed strategy can be combined with any tracking algorithm and has portability in embedded devices.The main contribution is the discussion of tracking initialization problem and a strategy to improve the accuracy of initial tracking box,rather than algorithmic innovation.