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Proceedings Paper

Visual tracking with L1-Grassmann manifold modeling
Author(s): Dimitris G. Chachlakis; Panos P. Markopoulos; Raj J. Muchhala; Andreas Savakis
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Paper 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.

Paper Details

Date Published: 5 May 2017
PDF: 10 pages
Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 1021102 (5 May 2017); doi: 10.1117/12.2263691
Show Author Affiliations
Dimitris G. Chachlakis, Rochester Institute of Technology (United States)
Panos P. Markopoulos, Rochester Institute of Technology (United States)
Raj J. Muchhala, Rochester Institute of Technology (United States)
Andreas Savakis, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 10211:
Compressive Sensing VI: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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