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所在平台: Udemy |
课程主页: https://www.udemy.com/course/object-tracking-using-python-and-opencv/
课程评论：没有评论
课程名称：使用Python和OpenCV进行目标跟踪 课程概述： 目标跟踪是计算机视觉的一个子领域，旨在在视频的连续帧中定位对象。其应用实例包括视频监控和安全系统，能够检测可疑行为；以及高速公路交通监控和足球比赛中运动员的活动分析。在足球比赛中，可以追踪运动员在比赛中的完整路线。本课程将带您进入这个领域，学习使用Python语言和OpenCV库的主要目标跟踪算法！您将了解12种算法的基本直觉，并逐步实施它们！课程结束时，您将能够将跟踪算法应用于视频，从而开发自己的项目。 将涵盖的算法包括： - Boosting - MIL（多实例学习） - KCF（核相关滤波器） - CSRT（具有信道和空间可靠性的判别相关滤波器） - MedianFlow - TLD（跟踪学习检测） - MOSSE（最小输出平方和误差） - Goturn（使用回归网络的通用目标跟踪） - Meanshift - CAMShift（连续自适应Meanshift） - 稀疏光流 - 密集光流 您将学习所有算法的基本直觉，然后在PyCharm IDE中实施和测试它们。课程的重点是实用性，因此理论内容不会过多，只涉及每种算法的基本方面。通过了解不同算法，您可以根据特定应用选择最佳方案，从而解决您所面临的问题。
Object tracking is a subarea of Computer Vision which aims to locate an object in successive frames of a video. An example of application is a video surveillance and security system, in which suspicious actions can be detected. Other examples are the monitoring of traffic on highways and also the analysis of the movement of players in a soccer match! In this last example, it is possible to trace the complete route that the player followed during the match.To take you to this area, in this course you will learn the main object tracking algorithms using the Python language and the OpenCV library! You will learn the basic intuition about 12 (twelve) algorithms and implement them step by step! At the end of the course you will know how to apply tracking algorithms applied to videos, so you will able to develop your own projects. The following algorithms will be covered: Boosting, MIL (Multiple Instance Learning), KCF (Kernel Correlation Filters), CSRT (Discriminative Correlation Filter with Channel and Spatial Reliability), MedianFlow, TLD (Tracking Learning Detection), MOSSE (Minimum Output Sum of Squared) Error), Goturn (Generic Object Tracking Using Regression Networks), Meanshift, CAMShift (Continuously Adaptive Meanshift), Optical Flow Sparse, and Optical Flow Dense.You'll learn the basic intuition about all algorithms and then, we'll implement and test them using PyCharm IDE. It's important to emphasize that the goal of the course is to be as practical as possible, so, don't expect too much from the theory since you are going to learn only the basic aspects of each algorithm. The purpose of showing all these algorithms is for you to have a view that different algorithms can be used according to the types of applications, so you can choose the best ones according to the problem you are trying to solve.
