Computational models of multiple object tracking (MOT) presuppose the existence of non-conceptual indexes in visual perception, and as a result predict that ID (identification) performance on MOT tasks should be no worse than tracking performance for the same stimuli. However, empirical evidence suggests that ID performance is worse than tracking performance in MOT. We propose a computational model of MOT that is able to account for several empirical results related to tracking performance without the use of indexes and thus avoids yoking tracking performance to ID performance. We also test our model empirically, contrasting it with an existing index-based model, and show that an assumption that avoids indexes and instead incorporates an explicit (rather than an implicit) mechanism for identity maintenance accounts well for the variation in ID performance with increasing number of targets in MOT with visually identical objects.