1. A tracking method comprising: for each of a plurality of frames in a sequence of frames: with a generic proposal extractor which detects locations of candidate object by searching the frame for features of objects in general, identifying a set of windows based on a computed probability, for each window, that the window contains an object based on at least one of edge and contour density in the frame, without predicting a category of the respective object, by setting at least one of: a maximum or total number of windows per frame; and a probability threshold, which excludes windows which are below a predefined threshold probability of containing an object; for each identified window, extracting a multi-dimensional feature representation of the window; with a trained detector, based on the extracted multi-dimensional feature representations, detecting a set of targets, each target in the set comprising a respective one of the windows that is predicted to contain an object in a selected category, a respective target-specific appearance model being generated for each of the targets; tracking at least one of the targets in the set for at least one subsequent frame, the tracking being based on the target-specific appearance model generated for that target, wherein at least one of the identifying of the set of windows, detecting targets, and tracking the targets is performed with a processor.