A novel visual tracking method using stochastic fractal search algorithm

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.