Facilitating multiple target tracking using semantic depth of field (SDOF)
Facilitating multiple target tracking using semantic depth of field (SDOF)
复制标题
使用语义景深 (SDOF) 促进多目标跟踪
DOI：
--
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Pourang Irani
中科院分区：
文献类型：
--
作者：
Nivedita R. Kadaba;Xing;Pourang Irani
雷达控制系统和监控应用的用户必须不断地从动态场景中提取必要的信息。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。然而，聚焦于多个运动目标并不容易，因为它非常容易失去连续性，特别是当对象位于非常密集或杂乱的背景中时。虽然已经开发了焦点+上下文显示来提高用户注意重要视觉信息的能力，但是这种技术还没有被应用于移动对象的可视化。在本文中，我们评估了焦点+上下文技术，称为语义景深(SDOF)，以促进多目标跟踪的任务的有效性。我们的研究结果表明，SDOF技术有更好的性能倾向，特别是在低对比度的场景中。
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.
影响因子：
--
作者：
Z. Pylyshyn;Vidal Annan
通讯作者：
Z. Pylyshyn;Vidal Annan