Abstract: We present the theory behind TOD (the Temporal Object Discoverer), a novel unsupervised system that uses only temporal information to discover objects across image sequences acquired by any number of uncalibrated cameras. The process is divided into three phases: (1) Extraction of each pixel's temporal signature, a partition of the pixel's observations into sets that stem from different objects; (2) Construction of a global schedule that explains the signatures in terms of the lifetimes of a set of quasi-static objects; (3) Mapping of each pixel's observations to objects in the schedule according to the pixel's temporal signature. Our Global Scheduling (GSched) algorithm provably constructs a valid and complete global schedule when certain observability criteria are met. Our Quasi-Static Labeling (QSL) algorithm uses the schedule created by GSched to produce the maximally-informative mapping of each pixel's observations onto the objects they stem from. Using GSched and QSL, TOD ignores distracting motion, correctly deals with complicated occlusions, and naturally groups observations across cameras. The sets of 2D masks recovered are suitable for unsupervised training and initialization of object recognition and tracking systems.