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首页> 外文期刊>Arabian Journal for Science and Engineering >Approximate Proximal Gradient-Based Correlation Filter for Target Tracking in Videos: A Unified Approach
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Approximate Proximal Gradient-Based Correlation Filter for Target Tracking in Videos: A Unified Approach

机译：用于视频中目标跟踪的基于近似近端梯度的相关滤波器：统一方法

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摘要

Video cameras are among the most commonly used devices throughout the world which results in imaging technology being one of the most important areas for research and development. Imaging technology requires constant research as it is used in crucial applications such as video conferencing and surveillance. In the field of image processing, motion detection and estimation are fundamental steps in extracting information on objects segmented from their backgrounds. In this paper, a cohesive approach is presented that uses two algorithms for motion estimation and detection. The proposed method is able to detect moving objects using maximum average correlation height (MACH) filter. Upon obtaining the accurate coordinates of an object of interest from the MACH filter, the next part of the algorithm starts tracking the object. For tracking, a particle filter is used to estimate the motion of the object using a Markov chain. To enhance the accuracy of particle filter, an approximate proximal gradient algorithm is employed for unconstrained minimization of the particles which restricts the tracking process to target templates (most essential information) only. Finally, a comparison between the proposed algorithm and recent similar algorithms is made that demonstrates the minimization of tracking errors using the proposed technique.
机译：摄像机是全世界最常用的设备之一，导致成像技术成为最重要的研发领域之一。影像技术需要用于视频会议和监视等关键应用，因此需要不断研究。在图像处理领域，运动检测和估计是提取有关从其背景分割的对象的信息的基本步骤。在本文中，提出了一种结合方法，该方法使用两种算法进行运动估计和检测。所提出的方法能够使用最大平均相关高度（MACH）滤波器检测运动物体。从MACH滤波器获得感兴趣对象的准确坐标后，算法的下一部分将开始跟踪该对象。为了进行跟踪，使用了粒子滤波器来使用马尔可夫链估计物体的运动。为了提高粒子过滤器的精度，采用了近似的近端梯度算法来无限制地最小化粒子，这将跟踪过程仅限制为目标模板（最基本的信息）。最后，将提出的算法与最近的类似算法进行比较，以证明使用提出的技术可使跟踪误差最小化。

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