We present an approach for ranking a collection of videos with overlapping fields of view. The ranking depends on how they allow to visualize as best as possible, i.e. with significant details, a trajectory query drawn in one of the videos. The proposed approach decomposes each video into cells and aims at estimating a correspondence map between cells from different videos using the linear correlation between their functions of activity. These latter are obtained during a training session by detecting objects in the videos and computing the coverage rate between the objects and the cells over time. The main idea is that two areas from two different videos that systematically offer presence of objects simultaneously are very likely to correspond to each other. Then, we use the correspondence between cells to find the reformulated trajectory in the other videos. Finally, we rank the videos based on the visibility they offer. We show promising results by testing three aspects: the correspondence maps, the reformulation and the ranking.