BBAuthors: Yang, M.Y.; Advisor: -; Participants: - (2017)
In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are highdimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a lowdimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result and deal with the missing data problem, we propose an error estimation to encourage the projected... 
