Nenavath Hathiram, Ashwini K, Jatoth Ravi Kumar, Mirjalili Seyedali
Department of Electronics and Communication Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, 501218, India.
National Institute of Technology, Warangal, 506004, India.
ISA Trans. 2022 Sep;128(Pt A):460-476. doi: 10.1016/j.isatra.2021.09.014. Epub 2021 Sep 24.
Visual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers.
视觉跟踪是多种计算机视觉应用中的重要任务之一。粒子滤波器（PF）在智能监控系统应用的视觉跟踪中被广泛使用，具有重大意义。但是PF的重采样过程会导致样本贫化，这会同时影响跟踪的精度。本文提出了一种基于正弦余弦算法（SCA）优化的PF的新跟踪技术，称为三角粒子滤波器（TPF），它包含三角正弦和余弦函数。一种用于改进三角粒子滤波器的正弦余弦算法中使用的目标粒子数量的增强方法包括在重采样步骤之前进行SCA。这一步确保了更广泛的粒子集。在视觉跟踪器基准（VOT）数据库上检查和评估了所提出的TPF跟踪器的性能。将所提出的TPF跟踪器与基于进化的方法（如蜘蛛猴优化辅助PF（SMO-PF）、基于萤火虫算法的PF（FAPF）方法、基于粒子群优化的PF（PSO-PF）和粒子滤波器）、最近的四种基于相关滤波器的跟踪器以及其他十种先进的跟踪方法进行了比较。我们证明，与比较的跟踪器相比，使用TPF的视觉跟踪提供了更一致和高效的跟踪结果。
