Recently metaheuristic algorithms have attracted the attention of many researchers in various disciplines for their simplicity of application and their efficiency. Visual tracking is one of the most promising fields of application of these methods, and although many approaches have been proposed, their main disadvantage is the convergence at local minima which make them unable to find the exact position. To overcome this drawback, we propose to use an algorithm that provides an efficient exploration of the search space, which is stochastic fractal search (SFS) algorithm. SFS is used as a localization method, to find the most similar candidate to a previous defined template. Standard kernel-based spatial color histogram of the object bounding box, is evaluated in order to model the object appearance. Subsequently, Bhattacharyya distance is measured between the two histograms of the model and the candidate to define the fitness function, in which optimization is sought. To assess fairly the robustness of our approach, we have evaluated its performance on 20 video sequences from the OTB-100 sequences dataset and compared it to 11 other state-of-the-art trackers. Quantitative and qualitative evaluations on challenging situations provided satisfying results of SFS-based tracker compared to other state-of-the-art algorithms.

中文翻译：

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一种基于随机分形搜索算法的视觉跟踪新方法

最近，元启发式算法因其应用的简单性和效率而引起了各个学科的许多研究人员的注意。视觉跟踪是这些方法最有前途的应用领域之一，尽管已经提出了许多方法，但它们的主要缺点是在局部最小值处收敛，使它们无法找到准确的位置。为了克服这个缺点，我们建议使用一种提供搜索空间有效探索的算法，即随机分形搜索 (SFS) 算法。SFS 用作定位方法，以找到与先前定义的模板最相似的候选者。评估对象边界框的基于标准内核的空间颜色直方图以对对象外观进行建模。随后，Bhattacharyya 距离是在模型的两个直方图和候选者之间测量的，以定义适应度函数，在其中寻求优化。为了公平地评估我们方法的稳健性，我们评估了它在来自 OTB-100 序列数据集的 20 个视频序列上的性能，并将其与其他 11 个最先进的跟踪器进行了比较。与其他最先进的算法相比，对具有挑战性的情况的定量和定性评估提供了基于 SFS 的跟踪器的令人满意的结果。