1. A method for tracking objects over a wide area, the method comprising: using a processor to generate a plurality of sets of local-track segments, wherein the plurality of sets of local-track segments respectively correspond to a plurality of objects captured in images taken by a respective plurality of stereo sensors, wherein generating each of the plurality of sets of local-track segments is based on generating, for each of the plurality of objects, a non-parametric probabilistic-shape model for such object; generating, for each of the plurality of objects, a part-based appearance representation for such object, wherein the part-based appearance representation includes generating adaptive and fixed image templates using global representations; generating, for each of the plurality of objects, a ground-based-depth representation for such object; and iteratively estimating ownership and motion parameters of each of the non-parametric probabilistic-shape model, the part-based appearance representation, and the ground-based-depth representation using an Expectation Maximalization algorithm; and designating at least one of the plurality of objects as a designated set of objects; combining, for each of the designated objects, its set of local-track segments along with space and time constraints to form a designated global track, wherein the space and time constraints are formed from ground-based, space-time cues.