论文标题
简化视频数据中对象跟踪的方法
Methods to Simplify Object Tracking in Video Data
论文作者
论文摘要
近年来,人们对分析视频数据中对象的运动的兴趣激增,这是学生将物理学概念与实验视频录制之类的有形内容连接起来的一种方式。存在各种软件供学生查看单个框架,然后单击对象来推断X,y位置。其中一些工具包括能够自动识别对象在框架中的位置的能力。但这并不罕见,尤其是当没有经验的用户正在录制视频并配置程序时,这些算法难以将其“锁定”到移动对象。在本文中,我们俩都提供一些一般建议,以帮助对象跟踪算法找到一个对象,并且我们提供了自己的算法,这些算法比当前正在使用的复杂图像处理算法更简单,更有效。这些算法不关注移动对象的“模板图像”,而是通过仅分析单个像素的颜色来区分对象和背景。这些算法内置在一个称为stemcoding对象跟踪器(http://go.osu.edu/objectTracker)的免费开源程序中,该程序在浏览器中起作用(无需下载),并且与包括Chromebook在内的各种操作系统兼容。
Recent years have seen an explosion of interest in analyzing the motion of objects in video data as a way for students to connect the concepts of physics to something tangible like a video recording of an experiment. A variety of software exists for students to look at individual frames and click on the object to infer the x,y position. Some of these tools include a capability to automatically identify the position of the object in the frame. But it is not unusual, especially when inexperienced users are recording the video and configuring the program, for these algorithms to struggle to "lock on" to the moving object. In this paper, we both include some general advice to help object tracking algorithms locate an object and we provide our own algorithms that are simpler and potentially more effective than the sophisticated image processing algorithms that are currently being used. These algorithms focus not on a "template image" of the moving object but instead distinguish between the object and the background by analyzing only the colors of individual pixels. These algorithms are built into a free and open source program called the STEMcoding Object Tracker (http://go.osu.edu/objecttracker) which works in the browser (without any downloads) and is compatible with a variety of operating systems including chromebooks.