首页> 外文会议>Conference on Visual Communications and Image Processing 2001 Jan 24-26, 2001, San Jose, USA >A Research Environment for Developing and Testing Object Tracking Algorithms
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A Research Environment for Developing and Testing Object Tracking Algorithms

机译:开发和测试目标跟踪算法的研究环境

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We present an integrated research environment (RAVEN) that we have developed for the purpose of developing and testing object tracking algorithms. As a Windows application, RAVEN provides a user interface for loading and viewing video sequences and interacting with the segmentation and object tracking algorithms, which are included at run time as plug-ins. The plug-ins interact with RAVEN via a programming interface, enabling algorithm developers to concentrate on their ideas rather than on the user interface. Over the past two years, RAVEN has greatly enhanced the productivity of our researchers, enabling them to create a variety of new algorithms and extend RAVEN's capabilities via plug-ins. Examples include several object tracking algorithms, a live-wire segmentation algorithm, a methodology for the evaluation of segmentation quality, and even a mediaprocessor implementation of an object tracker. After implementing an algorithm, RAVEN makes it easy to present the results since it provides several mask display modes and output options for both image and video. We have found that RAVEN facilitates the entire research process, from prototyping an algorithm to visualization of the results to a mediaprocessor implementation.
机译:我们提出了一个集成的研究环境(RAVEN),该环境是我们为开发和测试对象跟踪算法而开发的。作为Windows应用程序,RAVEN提供了一个用户界面,用于加载和查看视频序列以及与分段和对象跟踪算法进行交互,这些算法在运行时作为插件包含在内。这些插件通过编程界面与RAVEN进行交互,从而使算法开发人员可以专注于他们的想法,而不是用户界面。在过去的两年中,RAVEN极大地提高了研究人员的生产率,使他们能够创建各种新算法并通过插件扩展RAVEN的功能。示例包括几种对象跟踪算法,实时线分割算法,用于评估分割质量的方法,甚至是对象跟踪器的媒体处理器实现。实施算法后,RAVEN可以轻松呈现结果,因为它为图像和视频提供了多种蒙版显示模式和输出选项。我们发现,RAVEN促进了整个研究过程,从原型算法到结果可视化再到媒体处理器实现。

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