飞蛾扑火优化的尺度比例感知空间长期跟踪器

针对无人机长期跟踪过程中尺度变换导致目标丢失和跟踪精度低的问题，提出了一种基于飞蛾扑火优化(moth-flame optimization, MFO)的尺度比例感知空间长期跟踪器。首先，设计了高斯初始化以代替飞蛾扑火优化算法的随机初始化策略，降低优化算法在跟踪过程中的计算复杂度，减少算力浪费；其次，结合快速梯度直方图特征，构建了改进的飞蛾扑火优化跟踪器；然后，为了解决无人机航拍长期跟踪中目标尺度变化的问题，设计了一种自适应尺度变换的判别尺度空间跟踪(discriminative scale space tracking, DSST)算法，进一步提出了一种尺度比例感知空间跟踪器，解决了尺度滤波器中因长宽比固定而导致的跟踪漂移；同时，分析了滤波器响应峰值在各背景下的变化情况，提出了一种能反映环境变化下跟踪置信度的指标，并通过置信度将MFO优化跟踪框架与尺度比例感知空间跟踪器相结合，解决了尺度变化与长期跟踪目标丢失的问题；最后，在无人机长期跟踪数据集上开展了性能验证。结果表明：提出的算法可有效防止漂移现象的发生，提升跟踪效率；与目前跟踪领域中12种同类文献算法进行对比可知，提出的算法精度较高...

A scale-aware spatial long-term tracker based on moth-flame optimization

In view of the problems of target loss and low tracking accuracy due to scale transformation during long-term UAV tracking, a scale-aware spatial tracker based on moth-flame optimization (MFO) was proposed. First, the Gaussian initialization was used to replace the random initialization strategy of the original moth-flame optimization algorithm, so as to reduce the high computational complexity and waste of computing power of the optimization algorithm in the tracking problem. Second, on the basis of the characteristics of fast gradient histogram, an improved moth-flame optimization tracker was constructed. Then, considering the problem of target scale change under the long-term tracking of UAV aerial photography, a discriminative scale space tracking (DSST) algorithm combined with adaptive scale transformation was designed. A scale-aware spatial tracker was further proposed to solve the problem of tracking drift caused by the fixed aspect ratio of the scale filter. In addition, the variation of the filter response peak value under different backgrounds was analyzed, and an index that can reflect the tracking confidence under environmental changes was proposed. The moth-flame optimization tracking framework was combined with the scale-aware spatial tracker through confidence, which can solve the problems of scale change and target loss in long-term tracking. Finally, the performance of the algorithm was verified on the UAV long-term tracking dataset. Results show that the proposed algorithm can effectively prevent the occurrence of drift and improve the tracking efficiency. Compared with 12 similar algorithms in the tracking field, the proposed algorithm can effectively solve the scale change and the target loss of the long-term UAV tracking, and meet the requirement of real-time with high accuracy.