## 计算机代写|计算机视觉代写Computer Vision代考|COSC428

2023年2月3日

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## 计算机代写|计算机视觉代写Computer Vision代考|Moving Object Tracking

To track the moving object in the video is to detect and locate the same object in each frame of the video image. The following difficulties are often encountered in practical applications:

1. The object and the background are similar, and it is not easy to capture the difference between the two.
2. The appearance of the object itself changes with time. On the one hand, some objects are nonrigid, and their appearance will inevitably change with time; on the other hand, external conditions such as light will change over time, whether it is a rigid body or a nonrigid body.
3. During the tracking process, due to the change of the spatial position between the background and the object, the tracked object may be blocked, and the (complete) object information will not be obtained. In addition, tracking must take into account the accuracy of object positioning and the real-time nature of the application.

Moving object tracking often combines the location and representation of the object (this is mainly a bottom-up process that needs to overcome the effects of object appearance, orientation, lighting, and scale changes) and trajectory filtering and data fusion (this is a top-down process that requires consideration of the object’s motion characteristics, the use of various prior knowledge and motion models, and the promotion and evaluation of motion assumptions).

Moving object tracking can use many different methods, including contour-based tracking, region-based tracking, mask-based tracking, feature-based tracking, and motion information-based tracking. Tracking based on motion information is also divided into tracking using the continuity of motion information and tracking using the method of predicting the object location in the next frame to reduce the search range. Several commonly used techniques are introduced below, among which both Kalman filtering and particle filtering are methods to reduce the search range.

## 计算机代写|计算机视觉代写Computer Vision代考|Kalman Filter

When tracking an object in the current frame, it is often desirable to be able to predict its position in the subsequent frame, so that the previous information can be utilized in maximum and the minimum search in the subsequent frame can be performed. In addition, prediction is also helpful to solve the problems caused by short-term occlusion. To this end, it is necessary to continuously update the position and speed of the tracked object point:
$$\begin{gathered} x_i=x_{i-1}+v_{i-1} \ v_i=x_i-x_{i-1} \end{gathered}$$
Here one needs to obtain three quantities: the original position, the optimal estimate of the corresponding variable (model parameter) before the observation (with sup-script mark -), and the optimal estimate of the corresponding variable after the observation (with sup-script mark +). In addition, noise needs to be considered. If $m$ is used to represent the noise of position measurement and $n$ is used to represent the noise of velocity estimation, the above two equations become
$$\begin{gathered} x_i^{-}=x_{i-1}^{+}+v_{i-1}+m_{i-1} \ v_i^{-}=v_{i-1}^{+}+n_{i-1} \end{gathered}$$
When the velocity is constant and the noise is Gaussian noise, the optimal solution is
\begin{aligned} & x_i^{-}=x_{i-1}^{+} \ & \sigma_i^{-}=\sigma_{i-1}^{+} \end{aligned}

# 计算机视觉代考

## 计算机代写|计算机视觉代写Computer Vision代考|Moving Object Tracking

1. 物体和背景相似，要捕捉两者之间的差异并不容易。
2. 对象本身的外观随时间而变化。一方面，有些物体是非刚性的，随着时间的推移，它们的外观不可避免地会发生变化；另一方面，无论是刚体还是非刚体，光线等外部条件都会随时间发生变化。
3. 在跟踪过程中，由于背景与物体之间空间位置的变化，可能导致被跟踪物体被遮挡，无法获取到（完整的）物体信息。此外，跟踪必须考虑到对象定位的准确性和应用程序的实时性。

## 计算机代写|计算机视觉代写Computer Vision代考|Kalman Filter

$$x_i=x_{i-1}+v_{i-1} v_i=x_i-x_{i-1}$$

$$x_i^{-}=x_{i-1}^{+}+v_{i-1}+m_{i-1} v_i^{-}=v_{i-1}^{+}+n_{i-1}$$

$$x_i^{-}=x_{i-1}^{+} \quad \sigma_i^{-}=\sigma_{i-1}^{+}$$

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。