Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning

Joint Authors

Chen, Si
Lu, Xiaoshun
Chen, Xiaosen
Chen, Min
Chen, Jianghu
Wang, Dahan
Zhu, Shunzhi

Source

Mathematical Problems in Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-03-10

Country of Publication

Egypt

No. of Pages

17

Main Subjects

Civil Engineering

Abstract EN

Object tracking is a critical research in computer vision and has attracted significant attention over the past few years.

However, the traditional object tracking algorithms often suffer from the object drifting problem due to various challenging factors in complex environments such as object occlusion and background clutter.

This paper proposes a robust and effective object tracking algorithm, called MCM, which combines compressive sensing and online multiple instance learning in a multi-classifier fusion framework.

In this framework, we integrate the different discriminative classifiers by learning the varied and compressed feature vectors based on different random projection matrices.

And then an improved online multiple instance learning mechanism SMILE is adopted, which introduces the relative similarity to select and weight the instances in the positive bag.

The experiments show that the proposed algorithm can improve the performance of object tracking on the challenging video sequences.

American Psychological Association (APA)

Chen, Si& Lu, Xiaoshun& Chen, Xiaosen& Chen, Min& Chen, Jianghu& Wang, Dahan…[et al.]. 2020. Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1193331

Modern Language Association (MLA)

Chen, Si…[et al.]. Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning. Mathematical Problems in Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1193331

American Medical Association (AMA)

Chen, Si& Lu, Xiaoshun& Chen, Xiaosen& Chen, Min& Chen, Jianghu& Wang, Dahan…[et al.]. Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1193331

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1193331