论文标题
使用Kalman Filter从视频流进行对象跟踪中的实用程序和隐私
Utility and Privacy in Object Tracking from Video Stream using Kalman Filter
论文作者
论文摘要
在计算机视觉中跟踪对象是一个困难的问题。隐私和效用问题增加了此问题的额外复杂性。在这项工作中，我们考虑使用Kalman过滤在视频流中跟踪对象时保持隐私和实用程序的问题。我们提出的第一个方法可确保该对象的本地化准确性不会超过一定级别。我们的第二种方法确保同一对象的本地化准确性将始终保持在一定阈值之下。
Tracking objects in Computer Vision is a hard problem. Privacy and utility concerns adds an extra layer of complexity over this problem. In this work we consider the problem of maintaining privacy and utility while tracking an object in a video stream using Kalman filtering. Our first proposed method ensures that the localization accuracy of this object will not improve beyond a certain level. Our second method ensures that the localization accuracy of the same object will always remain under a certain threshold.
