5 May 2017 Visual tracking with L1-Grassmann manifold modeling
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Abstract
We present a novel method for robust tracking in video frame sequences via L1-Grassmann manifolds. The proposed method represents adaptively the target as a point on the Grassmann manifold, calculated by means of L1-norm Principal-Component Analysis (L1-PCA). For this purpose, an efficient algorithm for adaptive L1-PCA is presented. Our experimental studies illustrate that the presented tracking method, leveraging the outlier resistance of L1-PCA, demonstrates robustness against target occlusions and illumination variations.
Conference Presentation
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Dimitris G. Chachlakis, Panos P. Markopoulos, Raj J. Muchhala, Andreas Savakis, "Visual tracking with L1-Grassmann manifold modeling", Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 1021102 (5 May 2017); doi: 10.1117/12.2263691; https://doi.org/10.1117/12.2263691
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