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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.

今年有两个主要的结果。1）额外的实验表明，投射在物体上的阴影似乎是一个比遮挡更大的问题，这是在去年的计划中讨论的。该方法不能适应得足够快，并且将对象上的阴影部分视为背景。一个单独的过程来分类阴影已被开发，其结果被动态地纳入对象模型，以规避这个问题。通过动态调整识别对象的轮廓的分辨率，以更好地捕捉边界的不平坦部分，从而产生更真实的对象形状的轮廓，也提高了准确性。最后，该算法已经完全转移到GPU上，以获得更好的性能，允许处理> 500 Hz的高清视频。目前，正在根据这些结果编写会议论文和期刊论文。2）如去年提出的，已经研究了一种基于特征点的跟踪方法，特别是针对刚性物体。它利用了当物体移动或旋转时物体上的点之间的距离是相同的这一事实。通过在对象移动时跟踪这些点，可以丢弃偏离这些距离的点。通过假设对象可以（部分）包含在长方体中，随着对象的新侧面变得可见，可以将新点添加到对象模型中。结果表明，除了跟踪对象，它的姿态也可以恢复。该方法已开发使用2D和3D图像数据。