Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm

An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose wea...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience Vol. 2016; no. 2016; pp. 637 - 645-054
Main Authors: Wang, Li Jia, Zhang, Hua
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Limiteds 01.01.2016
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Subjects:
Algorithms
Ambiguity
Artificial Intelligence
Attention - physiology
Classifiers
Distance learning
Eye Movements - physiology
Feedback
Histograms
Humans
Learning
Learning - physiology
Machine learning
Mathematical research
Methods
Online Systems
Photic Stimulation
Probability
Strategy
Technology application
Tracking
Visual
China
ISSN:1687-5265, 1687-5273
Online Access:Get full text
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Summary:An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probability M times. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.
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Academic Editor: Manuel Graña
ISSN:1687-5265
1687-5273
DOI:10.1155/2016/3472184