In the past few decades, the field of image processing has seen a rapid advancement in the correlation filters, which serves as a very promising tool for object detection and recognition. Mostly, complex filter equations are used for deriving the correlation filters, leading to a filter solution in a closed loop. Selection of optimal tradeoff (OT) parameters is crucial for the effectiveness of correlation filters. This paper proposes extended particle swarm optimization (EPSO) technique for the optimal selection of OT parameters. The optimal solution is proposed based on two cost functions. The best result for each target is obtained by applying the optimization technique separately. The obtained results are compared with the conventional particle swarm optimization method for various test images belonging from different state-of-the-art datasets. The obtained results depict the performance of filters improved significantly using the proposed optimization method.

中文翻译：

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使用扩展粒子群优化技术优化相关滤波器

在过去的几十年里，图像处理领域的相关滤波器取得了快速的进步，它成为一种非常有前途的目标检测和识别工具。大多数情况下，复杂的滤波器方程用于推导相关滤波器，从而得出闭环中的滤波器解。最佳权衡（OT）参数的选择对于相关滤波器的有效性至关重要。本文提出了扩展粒子群优化（EPSO）技术来优化 OT 参数的选择。基于两个成本函数提出最优解决方案。每个目标的最佳结果是通过单独应用优化技术获得的。对于来自不同最新数据集的各种测试图像，将获得的结果与传统粒子群优化方法进行比较。获得的结果表明使用所提出的优化方法显着提高了滤波器的性能。