Visual tracking with L1-Grassmann manifold modeling

Dimitris G. Chachlakis; Panos P. Markopoulos; Raj J. Muchhala; Andreas Savakis

doi:10.1117/12.2263691

5 May 2017 Visual tracking with L1-Grassmann manifold modeling

Dimitris G. Chachlakis, Panos P. Markopoulos, Raj J. Muchhala, Andreas Savakis

Proceedings Volume 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics; 1021102 (2017) https://doi.org/10.1117/12.2263691
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

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

Citation Download Citation

Dimitris G. Chachlakis, Panos P. Markopoulos, Raj J. Muchhala, and 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); https://doi.org/10.1117/12.2263691

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