Visual object tracking is aprocess that continuously infers the state of a target from several unconstrained scenes. It is commonly formulated as a searching (or classification) problem that aims to identify the candidate that matches the target template the most as the tracking result. The target template is maintained over time and updated online once the tracking result is available. Prior to tracking at the current time

a set of candidates are sampled around the state of the target at the previoustime. Both the target template and candidates are represented by an appearance model. Then

a target searching strategy is employedto find the candidate that matches the template most as the tracking result. Although several excellent methods of visual tracking exist

this area remains an overwhelming research topic because of several unresolved challenging issues that arise from both template learning and appearance modeling. From the point of view of appearance modeling

exploiting several representative templates from online data is the core problem and plays a key role in complex scenes where the target state is being changed over time significantly. In the proposed tracking framework

several low-dimensional basis vectors called positive templates are learned from high-dimensional online data by using the online PCA algorithm. Several negative templates are then sampled according to the last tracking result. The most representative object templates are organized by combining both positive and negative templates

and the target candidate is well represented through the use of online learned integrative templates with some additive Gaussian-Laplacian noise. Finally

the maximum likelihood between the target candidate and real object is estimated. Thus

the tracker can capture accurate information on the real object in each frame.Reasonable arrangements of the template update strategy are used to enhance the object templates during tracking. The online integrative templates can exploit the most comprehensive information on the target object with positive and negative templates compared with the simplex positive template learning approach because the online negative template expansion operation generates strong magnetic anisotropy between the target candidate and background data. In other words

positive templates help the tracker find the most possible target while negative templates actively represent the background data to help the tracker avoid the drifting problem. Thus

the tracker maintains the good capability to identify the greatest possible target candidate easily. Extensive experiments are conducted to validate the new algorithm. The tracker can learn several comparative object templates and self-updates at a fixed period

adapt well to several variations caused by intrinsic or extrinsic factors (pose

illumination

occlusion

scaling

background cluttering

motion blur

etc.)

and maintain the capability to exhibit favorable performance. Although template learning with online PCA is a widely-used feature extraction method for computer vision problems(e.g.

visual object tracking) and its learned templates contain some representative information on the target object

it is not very representative and needs to be enhanced with some additional information on the object to adapt well to uncertain complex variations. In this paper

two core issues (online template learning and appearance modeling)in visual object tracking are studied. Detailed descriptions of an efficient template organization strategy and an accurate model representation technique are provided

and a novel visual object tracking framework is proposed. The proposed algorithm can automatically exploit several useful integrative templates of the object from online data and self-updates. Hence

model representation exhibits strong robustness and improved tracking accuracy. Experiments on many challenging image sequences demonstrate that the proposed method achieves the same and even better results when compared with several state-of-the-art tracking algorithms.