.png){kind=icon}
返回
Algorithmic fusion for more robust feature tracking
.png){kind=icon}
PRE
AI
DOI：10.1023/A:1019833915960.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a framework for merging the results of independent feature-based motion trackers using a classification based approach. We demonstrate the efficacy of the framework using corner trackers as an example. The major problem with such systems is generating ground truth data for training. We show how synthetic data can be used effectively to overcome this problem. Our combined system performs better in both dropouts and errors than a correspondence tracker, and had less than half the dropouts at the cost of moderate increase in error compared to a relaxation tracker.
Keyword:
feature tracking
motion analysis
combining multiple trackers
algorithmic fusion
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
