Inverse nonnegative local coordinate factorization for visual tracking

Liu, Fanghui; Zhou, Tao; Gong, Chen; Fu, Keren; Bai, Li; Yang, Jie

Abstract

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.

Citation

Liu, F., Zhou, T., Gong, C., Fu, K., Bai, L., & Yang, J. (in press). Inverse nonnegative local coordinate factorization for visual tracking. IEEE Transactions on Circuits and Systems for Video Technology, https://doi.org/10.1109/TCSVT.2017.2699676

• Journal Article Type: Article
• Acceptance Date: Apr 22, 2017
• Online Publication Date: Apr 28, 2017
• Deposit Date: Aug 3, 2017
• Journal: IEEE Transactions on Circuits and Systems for Video Technology
• Print ISSN: 1051-8215
• Electronic ISSN: 1051-8215
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/TCSVT.2017.2699676
• Keywords: Local coordinate constraint, inverse nonnegative matrix factorization, incremental update, visual tracking
• Public URL: https://nottingham-repository.worktribe.com/output/857739
• Publisher URL: http://ieeexplore.ieee.org/document/7914620/
• Additional Information: (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.