Online subspace learning in Grassmann manifold for moving object tracking in video
Paper i proceeding, 2008
This paper proposes a robust object tracking method in video where the time-varying principal components of object’s appearance are updated online. Instead of directly updating the PCA-based subspace using matrix decomposition, the subspace is updated by tracking on the Grassmann manifold. The object tracker performs two alternating processes: (a) online learning of principal component subspace; (b) tracking a moving object using the learned subspace and a particle filter. Learning a PCA-based subspace is performed by treating principal component decompositions as noisy measurements. The measurements are mapped onto the Lie algebra of the Grassmann manifold. The direction of movement of the subspace is then tracked in the Lie algebra using a Kalman filter. The filtered output is then mapped back onto the Grassmann surface to update the principal component-based subspace. This produces a more reliable learning of the subspace. Experiments have been conducted on face image sequences where heads were tilted in variable speed, partial face occlusion, along with changes in object depth and in illuminations. The results and evaluations have shown that the proposed method is robust against these changes when tracking moving objects.
time-varying subspace learning