Inverse nonnegative local coordinate factorization for visual tracking
Recently, nonnegative matrix factorization (NMF) with part based representation has been widely used for appearance modelling in visual tracking. Unfortunately, not all the targets can be successfully decomposed as "parts" unless some rigorous conditions are satisfied. To avoid this proble...
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Institute of Electrical and Electronics Engineers
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44634/ |
| _version_ | 1848796961730199552 |
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| author | Liu, Fanghui Zhou, Tao Gong, Chen Fu, Keren Bai, Li Yang, Jie |
| author_facet | Liu, Fanghui Zhou, Tao Gong, Chen Fu, Keren Bai, Li Yang, Jie |
| author_sort | Liu, Fanghui |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Recently, nonnegative matrix factorization (NMF) with part based representation has been widely used for appearance modelling in visual tracking. Unfortunately, not all the targets can be successfully decomposed as "parts" unless some rigorous conditions are satisfied. To avoid this problem, this paper introduces NMF's variants into the visual tracking framework in the view of data clustering for appearance modelling. Firstly, an initial target appearance model based on NMF is proposed to describe the target's appearance with the incorporated local coordinate factorization constraint, orthogonality of the bases, and L1,1 norm regularized sparse residual error constraint. Secondly, an inverse NMF model is proposed, in which each learned base vector is regarded as a clustering center in a low-dimensional subspace. Potential target samples (from the foreground) will be clustered around base vectors; while the candidate samples (from the background) are very likely to spread irregularly over the entire clustering space. Such difference can be fully exploited by the inverse NMF model to produce more discriminative encoding vectors than the conventional NMF method. Further, incremental updating model is introduced into the tracking framework for online updating the initial appearance model. Experiments on Object Tracking Benchmark (OTB) suggest that our tracker is able to achieve promising performance when compared to some state-of-the-art methods in deformation, occlusion, and other challenging situations. |
| first_indexed | 2025-11-14T19:56:18Z |
| format | Article |
| id | nottingham-44634 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:56:18Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
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| spelling | nottingham-446342020-05-04T18:43:17Z https://eprints.nottingham.ac.uk/44634/ Inverse nonnegative local coordinate factorization for visual tracking Liu, Fanghui Zhou, Tao Gong, Chen Fu, Keren Bai, Li Yang, Jie Recently, nonnegative matrix factorization (NMF) with part based representation has been widely used for appearance modelling in visual tracking. Unfortunately, not all the targets can be successfully decomposed as "parts" unless some rigorous conditions are satisfied. To avoid this problem, this paper introduces NMF's variants into the visual tracking framework in the view of data clustering for appearance modelling. Firstly, an initial target appearance model based on NMF is proposed to describe the target's appearance with the incorporated local coordinate factorization constraint, orthogonality of the bases, and L1,1 norm regularized sparse residual error constraint. Secondly, an inverse NMF model is proposed, in which each learned base vector is regarded as a clustering center in a low-dimensional subspace. Potential target samples (from the foreground) will be clustered around base vectors; while the candidate samples (from the background) are very likely to spread irregularly over the entire clustering space. Such difference can be fully exploited by the inverse NMF model to produce more discriminative encoding vectors than the conventional NMF method. Further, incremental updating model is introduced into the tracking framework for online updating the initial appearance model. Experiments on Object Tracking Benchmark (OTB) suggest that our tracker is able to achieve promising performance when compared to some state-of-the-art methods in deformation, occlusion, and other challenging situations. Institute of Electrical and Electronics Engineers 2017-04-28 Article PeerReviewed Liu, Fanghui, Zhou, Tao, Gong, Chen, Fu, Keren, Bai, Li and Yang, Jie (2017) Inverse nonnegative local coordinate factorization for visual tracking. IEEE Transactions on Circuits and Systems for Video Technology . ISSN 1051-8215 Local coordinate constraint inverse nonnegative matrix factorization incremental update visual tracking http://ieeexplore.ieee.org/document/7914620/ doi:10.1109/TCSVT.2017.2699676 doi:10.1109/TCSVT.2017.2699676 |
| spellingShingle | Local coordinate constraint inverse nonnegative matrix factorization incremental update visual tracking Liu, Fanghui Zhou, Tao Gong, Chen Fu, Keren Bai, Li Yang, Jie Inverse nonnegative local coordinate factorization for visual tracking |
| title | Inverse nonnegative local coordinate factorization for visual tracking |
| title_full | Inverse nonnegative local coordinate factorization for visual tracking |
| title_fullStr | Inverse nonnegative local coordinate factorization for visual tracking |
| title_full_unstemmed | Inverse nonnegative local coordinate factorization for visual tracking |
| title_short | Inverse nonnegative local coordinate factorization for visual tracking |
| title_sort | inverse nonnegative local coordinate factorization for visual tracking |
| topic | Local coordinate constraint inverse nonnegative matrix factorization incremental update visual tracking |
| url | https://eprints.nottingham.ac.uk/44634/ https://eprints.nottingham.ac.uk/44634/ https://eprints.nottingham.ac.uk/44634/ |