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Facilitating Multiple Target Tracking using Semantic Depth of Field (SDOF)

机译:使用语义景深(SDOF)促进多目标跟踪

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Users of radar control systems and monitoring applications have to constantly extract essential information from dynamic scenes. In these environments a critical and elemental task consists of tracking multiple targets that are moving simultaneously. However, focusing on multiple moving targets is not trivial as it is very easy to lose continuity, particularly when the objects are situated within a very dense or cluttered background. While focus+context displays have been developed to improve users' ability to attend to important visual information, such techniques have not been applied to the visualization of moving objects. In this paper we evaluate the effectiveness of a focus+context technique, referred to as Semantic Depth of Field (SDOF), to the task of facilitating multiple target tracking. Results of our studies show an inclination for better performance with SDOF techniques, especially in low contrast scenarios.
机译:雷达控制系统和监视应用程序的用户必须不断地从动态场景中提取基本信息。在这些环境中,一项关键而基本的任务是跟踪同时移动的多个目标。但是,专注于多个移动目标并非易事,因为它很容易失去连续性,尤其是当对象位于非常密集或混乱的背景中时。尽管已经开发了焦点+上下文显示来提高用户关注重要视觉信息的能力,但是这种技术尚未应用于移动对象的可视化。在本文中,我们评估了聚焦+上下文技术(称为语义景深(SDOF))对促进多目标跟踪的有效性。我们的研究结果表明,倾向于使用SDOF技术获得更好的性能,尤其是在低对比度情况下。

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