Patent ID: 8548195

Claim:
A tracking method for tracking a specific subject in a frame image of a video sequence by using three observation models I, II and III, the tracking method being implemented on a microprocessor which performs the steps comprising: (a) detecting one character part from a frame image of a video sequence; (b) performing online learning on observation model I with at least one frame image of D 1 frame images prior to an t−1 th frame image that is input, performing online learning on observation model II with at least one frame image of D 2 frame images prior to the t−1 th frame image that is input, and performing offline training on observation model III with at least one frame image of D 3 frame images prior to the t−1 th frame image that is input; wherein t is a natural number, representing the serial number of the frame image; D 1 , D 2 , and D 3 , are natural numbers representing a life span of observation models I, II and III, respectively; (c) aiming at a t th frame image, using the three observation models, in order I, II, III, to update a sampling weight of specific subject samples in a sample set representing a candidate target; (d) judging whether the sampling weight finally updated from the observation model III in step (c) exceeds a first preset threshold; if it exceeds the first threshold, outputting a size and a position of the specific subject in the t th frame calculated based on the size and position of the specific subject samples in the sample set; if it does not exceed the first threshold, discarding the candidate target; and (e) performing steps (b) to (d) on a t+1 th frame image, wherein D 1 is less than D 2 which is less than D 3 , wherein N 1 , N 2 , N 3 are natural numbers representing a number of the specific subject samples in the sample set used for observation models I, II and III, respectively, and N 1 is greater than N 2 which is greater than N 3 , and wherein the process of performing online learning on observation model I includes the steps of: (f) collecting positive example samples and counterexample samples from previous D 1 frame images; and (g) based on the collected positive example samples and counterexample samples, determining various parameters adopted in the observation model I.