1. An object tracking method for a non-overlapping sensor network, applicable to a sensor network with a plurality of sensors, said method having a training phase and a detection phase, and comprising: in said training phase, obtaining a plurality of measured data through each of said plurality of sensors as training samples; marking out at least an entrance or exit within a measuring range of each of said plurality of sensors; estimating at least three characteristic functions related to an object to be tracked, including sensor spatial correlation function, moving time difference function and appearance similarity function, via an automatic learning method; and in said detection phase, using said at least three characteristic functions as basis for tracking said object and linking relationship of said object; wherein said automatic learning method is a recursive learning strategy; and wherein said recursive learning strategy further includes: (a) for each entering event occurring at each entrance or exit of each of said plurality of sensors, recording all leaving events during a past period in a training sample space; (b) with existing samples in said training sample space, estimating an entrance or exit correlation probability function, a moving time difference probability function and an appearance similarity difference probability function; (c) observing said appearance similarity difference probability function and removing data belonging to outliers statistically; (d) with remaining data, updating said moving time difference probability function and said appearance similarity difference probability function; and repeating steps (c) and (d) until said moving time difference probability function converges.