様々な属性を有する対象物の高速トラッキング

今年有两个主要的结果。1)更多的实验表明，投射在物体上的阴影似乎是一个比遮挡更大的问题，这在去年的计划中已经讨论过了。该方法不能很快地适应，将物体上的阴影部分视为背景。已经开发了一种单独的阴影分类过程，其结果被动态地合并到对象模型中以避免这个问题。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，从而产生更真实于对象形状的轮廓，也提高了精度。最后，该算法已经完全移到了GPU上，以获得显著更好的性能，允许处理&gt；500 hz高清视频。目前正在根据这些结果准备一篇会议论文和一篇期刊论文。2)如去年提出的那样，研究了一种基于特征点的跟踪方法，特别是针对刚体目标。它利用了当对象移动或旋转时对象上的点之间的距离相同的事实。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，当对象的新侧面变得可见时，可以向对象模型添加新的点。结果表明，除了跟踪目标外，还可以恢复目标的姿态。该方法是使用2D和3D图像数据开发的。

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.