Patch Based Multiple Instance Learning Algorithm for Object Tracking
Joint Authors
Wang, Li Jia
Zhang, Hua
Wang, Zhenjie
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-22
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
To deal with the problems of illumination changes or pose variations and serious partial occlusion, patch based multiple instance learning (P-MIL) algorithm is proposed.
The algorithm divides an object into many blocks.
Then, the online MIL algorithm is applied on each block for obtaining strong classifier.
The algorithm takes account of both the average classification score and classification scores of all the blocks for detecting the object.
In particular, compared with the whole object based MIL algorithm, the P-MIL algorithm detects the object according to the unoccluded patches when partial occlusion occurs.
After detecting the object, the learning rates for updating weak classifiers’ parameters are adaptively tuned.
The classifier updating strategy avoids overupdating and underupdating the parameters.
Finally, the proposed method is compared with other state-of-the-art algorithms on several classical videos.
The experiment results illustrate that the proposed method performs well especially in case of illumination changes or pose variations and partial occlusion.
Moreover, the algorithm realizes real-time object tracking.
American Psychological Association (APA)
Wang, Zhenjie& Wang, Li Jia& Zhang, Hua. 2017. Patch Based Multiple Instance Learning Algorithm for Object Tracking. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1139851
Modern Language Association (MLA)
Wang, Zhenjie…[et al.]. Patch Based Multiple Instance Learning Algorithm for Object Tracking. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1139851
American Medical Association (AMA)
Wang, Zhenjie& Wang, Li Jia& Zhang, Hua. Patch Based Multiple Instance Learning Algorithm for Object Tracking. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1139851
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139851