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Efficient visual tracking approach via whale optimizer and corrected background weighted histogram

机译：高效的视觉跟踪方法通过鲸鲸优化器和校正背景加权直方图

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摘要

There are mainly two components in almost every visual object tracking algorithm, which are the object presentation and the searching mechanism. In the literature, metaheuristic algorithms have been recently, widely used as searching method in visual tracking. In fact, the more discriminative is their combined object presentation the better is their precision. Salient background information is often inside the first detected region of the object, which may decrease considerably the tracking performance. In this work, we combine a powerful metaheuristic searching method, whale optimization algorithm (WOA), with the discriminative presentation named as corrected background weighted histogram (CBWH) to improve the tracking accuracy in difficult environments. WOA algorithm is used because of its strong searching ability, which enable it avoid being trapped in local optimum, and also its lowest computation cost compared to other metaheuristic algorithms. The WOA sensitivity parameters are studied experimentally to be adjusted in visual tracking. For a fair assessment of the proposed approach, its performance has been evaluated on 20 video sequences, from the famous object tracking benchmark OTB15, and compared with six (6) other state-of-art trackers. The evaluations were carried quantitatively and qualitatively on challenging situations and have provided satisfying results. The experimental results shows that WOA combined with CBWH can lead to further improvement, and better success rate in tracking.
机译：几乎每种可视对象跟踪算法中主要有两个组件，这是对象呈现和搜索机制。在文献中，最近已经广泛用作视觉跟踪中的搜索方法的成群质识别算法。事实上，歧视性越多是他们的组合对象呈现，它们的精确度越好。突出背景信息通常在对象的第一检测到的区域内部，这可以显着降低跟踪性能。在这项工作中，我们结合了强大的成像搜索方法，鲸鱼优化算法（WOA），判别呈现作为校正的背景加权直方图（CBWH），以提高困难环境中的跟踪精度。 WOA算法是由于其强大的搜索能力，使其能够避免被困在局部最佳状态，并且与其他成帧算法相比其最低计算成本。实验研究了WOA敏感性参数，以便在视觉跟踪中进行调整。对于对所提出的方法进行公平评估，它的性能已经在20个视频序列中评估了来自着名的物镜跟踪基准OTB15，并与六（6）个其他最先进的追踪器相比。评估是定量和定性地进行充满挑战的情况，并提供令人满意的结果。实验结果表明，WOA与CBWH相结合可导致进一步提高，以及更好的跟踪成功率。

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