Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30915
Title: Real-time object tracking via online discriminative feature selection
Authors: Zhang, K
Zhang, L 
Yang, MH
Keywords: Multiple instance learning
Object tracking
Online boosting
Supervised learning
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2013, v. 22, no. 12, 6576884, p. 4664-4677 How to cite?
Journal: IEEE transactions on image processing 
Abstract: Most tracking-by-detection algorithms train discriminative classifiers to separate target objects from their surrounding background. In this setting, noisy samples are likely to be included when they are not properly sampled, thereby causing visual drift. The multiple instance learning (MIL) paradigm has been recently applied to alleviate this problem. However, important prior information of instance labels and the most correct positive instance (i.e., the tracking result in the current frame) can be exploited using a novel formulation much simpler than an MIL approach. In this paper, we show that integrating such prior information into a supervised learning algorithm can handle visual drift more effectively and efficiently than the existing MIL tracker. We present an online discriminative feature selection algorithm that optimizes the objective function in the steepest ascent direction with respect to the positive samples while in the steepest descent direction with respect to the negative ones. Therefore, the trained classifier directly couples its score with the importance of samples, leading to a more robust and efficient tracker. Numerous experimental evaluations with state-of-the-art algorithms on challenging sequences demonstrate the merits of the proposed algorithm.
URI: http://hdl.handle.net/10397/30915
ISSN: 1057-7149
EISSN: 1941-0042
DOI: 10.1109/TIP.2013.2277800
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

77
Last Week
1
Last month
4
Citations as of Aug 10, 2017

WEB OF SCIENCETM
Citations

58
Last Week
1
Last month
1
Citations as of Aug 12, 2017

Page view(s)

45
Last Week
5
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.