Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/479314
Title: Robust Object Tracking with Online Multiple Instance Learning
Authors: Babenko, Boris;Ming-Hsuan Yang;Belongie, Serge
subject: Science & Technology
Year: 2011
Abstract: In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called "tracking by detection" has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.
Description: 
URI: http://localhost/handle/Hannan/284198
http://localhost/handle/Hannan/479314
Appears in Collections:2011

Files in This Item:
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AL1935936.pdf2.24 MBAdobe PDF
Title: Robust Object Tracking with Online Multiple Instance Learning
Authors: Babenko, Boris;Ming-Hsuan Yang;Belongie, Serge
subject: Science & Technology
Year: 2011
Abstract: In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called "tracking by detection" has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.
Description: 
URI: http://localhost/handle/Hannan/284198
http://localhost/handle/Hannan/479314
Appears in Collections:2011

Files in This Item:
File SizeFormat 
AL1935936.pdf2.24 MBAdobe PDF
Title: Robust Object Tracking with Online Multiple Instance Learning
Authors: Babenko, Boris;Ming-Hsuan Yang;Belongie, Serge
subject: Science & Technology
Year: 2011
Abstract: In this paper, we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques called "tracking by detection" has been shown to give promising results at real-time speeds. These methods train a discriminative classifier in an online manner to separate the object from the background. This classifier bootstraps itself by using the current tracker state to extract positive and negative examples from the current frame. Slight inaccuracies in the tracker can therefore lead to incorrectly labeled training examples, which degrade the classifier and can cause drift. In this paper, we show that using Multiple Instance Learning (MIL) instead of traditional supervised learning avoids these problems and can therefore lead to a more robust tracker with fewer parameter tweaks. We propose a novel online MIL algorithm for object tracking that achieves superior results with real-time performance. We present thorough experimental results (both qualitative and quantitative) on a number of challenging video clips.
Description: 
URI: http://localhost/handle/Hannan/284198
http://localhost/handle/Hannan/479314
Appears in Collections:2011

Files in This Item:
File SizeFormat 
AL1935936.pdf2.24 MBAdobe PDF