Xin-Bin Luo

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We propose an online tracking algorithm in which the object tracking is achieved by using subspace learning and non-negative matrix factorization (NMF) under the partile filtering framework. The object appearance is modeled by a non-negative combination of non-negative components learned from examples observed in previous frames. In order to robust tracking(More)
Extracting the motion patterns from videos is a basic task in video surveillance and has become an active research area. In this paper, we propose a novel approach for discovering motion patterns in a scene observed by one or two cameras. The chaos theory is employed to compute the chaotic invariant features (CIFs) after obtaining all the trajectories. The(More)
In this paper, we cast tracking as a novel multi-task learning problem and exploit various types of visual features. We use an on-line feature selection mechanism based on the two-class variance ratio measure, applied to log likelihood distributions computed with respect to a given feature from samples of object and background pixels. The proposed method is(More)
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