A tracking method for tracking a specific subject in a frame image of a video sequence by using m observation models, wherein the observation models are classifiers into which samples are input and from which observation likelihood is output; the tracking method comprising the steps of: (a) detecting one specific subject from a frame image of a video sequence; (b) performing online learning or offline training on a k th observation model with at least one frame image of D k frame images prior to an input t-1 th frame image, wherein the k th observation model is one of the m observation models, with k ranging from 1 to m, t is a natural number, representing the serial number of the frame image; k is a natural number, representing the serial number of the observation model; and D k is a natural number, representing a life span of the k th observation model, and wherein the m-1 or less observation models perform online learning, and the m th observation model has been trained offline; (c) investigating a t th frame image, by combining the m observation models in a cascade particle filter mode to update the sampling weight of samples which are acquired through random sampling around the specific subject, wherein the sampling weight is the weight of particles of the specific subject, and wherein, in the combining process of the m observation models in a cascade particle filter mode, the sampling weight of particles of the specific subject in the current frame is updated based on one observation model prior to the current frame, and a preset number of samples are newly selected through a resampling process, and the current observation model updates the sampling weight of particles of the specific subject in the current frame about the preset number of samples; (d) judging whether the sampling weight finally updated from the m th observation model exceeds a first preset threshold in step (c), if it exceeds the first threshold, outputting the size and position of the specific subject in the current frame calculated based on the size and position of the samples set of the specific subject samples; 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 the life span refers to a learning period and a service period of the observation models, with the lifespan of the offline training being of infinite length, and the lifespan of the online learning being of length one or five. (a) detecting one specific subject from a frame image of a video sequence; (b) performing online learning or offline training on a k th observation model with at least one frame image of D k frame images prior to an input t-1 th frame image, wherein the k th observation model is one of the m observation models, with k ranging from 1 to m, t is a natural number, representing the serial number of the frame image; k is a natural number, representing the serial number of the observation model; and D k is a natural number, representing a life span of the k th observation model, and wherein the m-1 or less observation models perform online learning, and the m th observation model has been trained offline; (c) investigating a t th frame image, by combining the m observation models in a cascade particle filter mode to update the sampling weight of samples which are acquired through random sampling around the specific subject, wherein the sampling weight is the weight of particles of the specific subject, and wherein, in the combining process of the m observation models in a cascade particle filter mode, the sampling weight of particles of the specific subject in the current frame is updated based on one observation model prior to the current frame, and a preset number of samples are newly selected through a resampling process, and the current observation model updates the sampling weight of particles of the specific subject in the current frame about the preset number of samples; (d) judging whether the sampling weight finally updated from the m th observation model exceeds a first preset threshold in step (c), if it exceeds the first threshold, outputting the size and position of the specific subject in the current frame calculated based on the size and position of the samples set of the specific subject samples; 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,