The New Kernel-based Multiple Instances Learning Algorithm for Object Tracking

Title:The New Kernel-based Multiple Instances Learning Algorithm for Object Tracking

Volume: 18

Author(s): Hua Zhang and Lijia Wang*

Affiliation:

• Hebei Science and Technology Department Intelligent Manufacturing Institute Shijiazhuang China

Abstract: Background: Visual tracking is a crucial component of computer vision systems.

Objective: To deal with the problems of occlusion, pose variation, and illumination in long-time tracking, we propose a new kernel-based multiple instances learning tracker.

Method: The tracker captures five positive bags, including the occlusion bag, pose bag, illumination bag, scale bag, and object bag, to deal with the appearance changes of an object in a complex environment. A Gaussian kernel function is used to compute the inner product for selecting the powerful weak classifiers, which further improves the efficiency of the tracker. Moreover, the tracking situation is determined by using these five classifiers, and the correlating classifiers are updated.

Results: The experimental results show that the proposed algorithm is robust in terms of occlusion and various appearance changes.

Conclusion: The proposed algorithm preforms well in complex situations.

Cite this article as:

Zhang Hua and Wang Lijia*, The New Kernel-based Multiple Instances Learning Algorithm for Object Tracking, Recent Patents on Engineering 2024; 18 () : e061023221824 . https://dx.doi.org/10.2174/0118722121236196230925114428