Models for computer vision are commonly defined either w.r.t. low-level concepts such as pixels that are to be grouped, or w.r.t. high-level concepts such as semantic objects that are to be detected and tracked. Combining bottom-up grouping with top-down detection and tracking, although highly desirable, is a challenging problem. We state this joint problem as a co-clustering problem that is principled and tractable by existing algorithms. We demonstrate the effectiveness of this approach by combining bottom-up motion segmentation by grouping of point trajectories with high-level multiple object tracking by clustering of bounding boxes. We show that solving the joint problem is beneficial at the low-level, in terms of the FBMS59 motion segmentation benchmark, and at the high-level, in terms of the Multiple Object Tracking benchmarks MOT15, MOT16, and the MOT17 challenge, and is state-of-the-art in some metrics.

中文翻译：

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基于关联共聚的运动分割与多目标跟踪

通常用低级概念（例如要分组的像素）或高级概念（例如要检测和跟踪的语义对象）定义计算机视觉的模型。尽管自上而下的分组与自上而下的检测和跟踪相结合，尽管是非常理想的，但是这是一个具有挑战性的问题。我们将这个联合问题声明为一个共聚问题，这是现有算法在原则上和易于解决的问题。我们通过结合点轨迹的自下而上运动分割和包围盒聚类的高级多目标跟踪，证明了该方法的有效性。我们显示，就FBMS59运动分割基准而言，解决联合问题在低级方面是有益的，而在多目标跟踪基准MOT15，MOT16，