様々な属性を有する対象物の高速トラッキング

对各种属性的物体进行高速跟踪

基本信息

批准号：
12F02740
负责人：
石川正俊
金额：
$ 1.41万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2012
资助国家：
日本
起止时间：
2012-04-01 至 2015-03-31
项目状态：
已结题

项目摘要

There are two main results from this year.1) Additional experiments revealed that shadows cast on the object seem to be a bigger problem than occlusions, which was discussed in last year’s plan. The method was not able to adapt quickly enough, and deemed the shaded part on the object to be background. A separate process to classify shadows has been developed and its results are dynamically incorporated into the object model to circumvent this problem. The accuracy has also been improved by dynamically adapting the resolution of the contour identifying the object to better capture uneven parts of the boundary resulting in a contour that is more true to the object shape. Finally the algorithm has entirely been moved to the GPU for significantly better performance, allowing for processing of > 500 Hz HD video. Currently a conference paper and a journal paper are being prepared based on these results.2) As proposed last year, a feature point based tracking method has been investigated, specifically targeting rigid objects. It exploits the fact that the distances between points on the object are the same when the object moves or rotates. By tracking these points as the object moves, points that diverge from these distances can be discarded. By assuming the object can be (partially) contained in a cuboid, new points can be added to the object model as new sides of the object become visible. It was shown that in addition to tracking the object, its pose could also be recovered. This method has been developed using both 2D and 3D image data.

今年有两个主要结果。1）其他实验表明，对物体上施放的阴影似乎比闭塞更大的问题，这在去年的计划中进行了讨论。该方法无法足够快地适应，并认为对象上的阴影部分是背景。已经开发了一个单独的分类阴影的过程，并将其结果动态合并到对象模型中以避免此问题。通过动态调整轮廓识别对象的分辨率以更好地捕获边界的不平坦部分的轮廓的分辨率，从而提高了精度，从而使对象形状更真实。最后，该算法已完全移至GPU，以明显更好地性能，从而可以处理> 500 Hz HD视频。目前，正在根据这些结果制备会议论文和期刊论文。2）如去年所提出的那样，已经研究了一种基于功能点的跟踪方法，专门针对刚性对象。它探讨了一个事实，当对象移动或旋转时，对象上点之间的距离相同。通过在对象移动时跟踪这些点，可以丢弃与这些距离不同的点。通过假设对象可以（部分）包含在立方体中，可以将新点添加到对象模型中，因为对象的新侧面变得可见。结果表明，除了跟踪对象外，还可以恢复其姿势。使用2D和3D图像数据开发了此方法。