Visual object tracking remains a challenging task in computer vision although important progress has been made in the past decades. Particle filter (PF) is now a standard framework for solving non-linear/non-Gaussian problems, especially in visual object tracking. This study proposes an ant colony optimisation (ACO)-based iterative PF for object tracking. In the proposed method, the basic idea of ACO is used to simulate the behaviour of a particle moving toward the posterior distribution. Such idea is incorporated into the particle filtering framework in order to overcome the well-known particle impoverishment problem. An iterative unscented Kalman filter is used to design a proposal distribution for particle generation in order to generate better predicted sample states. For the likelihood model, the authors adopt the locality sensitive histogram to model the appearance of the target object, which can better handle the illumination variation during tracking. The experimental results demonstrate that the proposed tracker shows better performance than the other tracking methods.

中文翻译：

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通过迭代蚂蚁粒子滤波的视觉对象跟踪

视觉对象跟踪在计算机视觉中仍然是一项艰巨的任务，尽管在过去几十年中已经取得了重要进展。粒子滤波器（PF）现在是解决非线性/非高斯问题的标准框架，尤其是在视觉对象跟踪中。这项研究提出了一种基于蚁群优化（ACO）的迭代PF用于对象跟踪。在提出的方法中，ACO的基本思想用于模拟粒子向后分布移动的行为。为了克服众所周知的粒子贫乏问题，这种思想被并入了粒子过滤框架。迭代无味卡尔曼滤波器用于设计用于粒子生成的建议分布，以便生成更好的预测样本状态。对于似然模型，作者采用局部敏感直方图对目标对象的外观进行建模，可以更好地处理跟踪过程中的光照变化。实验结果表明，提出的跟踪器表现出比其他跟踪方法更好的性能。