Joo Seong-Wook, Chellappa Rama
IEEE Trans Image Process. 2007 Nov;16(11):2849-54. doi: 10.1109/tip.2007.906254.
In multiple-object tracking applications, it is essential to address the problem of associating targets and observation data. For visual tracking of multiple targets which involves objects that split and merge, a target may be associated with multiple measurements and many targets may be associated with a single measurement. The space of such data association is exponential in the number of targets and exhaustive enumeration is impractical. We pose the association problem as a bipartite graph edge covering problem given the targets and the object detection information. We propose an efficient method of maintaining multiple association hypotheses with the highest probabilities over all possible histories of associations. Our approach handles objects entering and exiting the field of view, merging and splitting objects, as well as objects that are detected as fragmented parts. Experimental results are given for tracking multiple players in a soccer game and for tracking people with complex interaction in a surveillance setting. It is shown through quantitative evaluation that our method tracks through varying degrees of interactions among the targets with high success rate.
在多目标跟踪应用中，解决目标与观测数据的关联问题至关重要。对于涉及物体分裂和合并的多目标视觉跟踪，一个目标可能与多个测量值相关联，并且许多目标可能与单个测量值相关联。这种数据关联空间在目标数量上呈指数级增长，穷举枚举是不切实际的。给定目标和目标检测信息，我们将关联问题表述为二分图边覆盖问题。我们提出了一种有效的方法，在所有可能的关联历史中维护具有最高概率的多个关联假设。我们的方法能够处理进入和离开视野的物体、合并和分裂的物体，以及被检测为碎片部分的物体。给出了在足球比赛中跟踪多个球员以及在监控场景中跟踪具有复杂交互的人员的实验结果。通过定量评估表明，我们的方法在目标之间存在不同程度交互的情况下能够以高成功率进行跟踪。
