Automated cell tracking using in vivo imagery is difficult, in general, due to the noise inherent in the imaging process, occlusions, varied cell appearance over time, motion of other tissue (distractors), and cells traveling in and out of the image plane. For certain types of cells these problems are exacerbated due to erratic motion patterns. In this paper, we introduce the Radial Flow Transform, which provides motion estimates for objects of interest in a scene without explicitly tracking each object. The transform is robust to misdetected objects, temporally-disjoint motion events, and can represent multiple directions of flow at a single location. We provide operations to convert to and from a vector field representation. This allows for intuitive reasoning about the motion patterns in a scene. We demonstrate results on synthetic data and in vivo microscopy video of a mouse liver.