Cluster-based dictionary learning and locality-constrained sparse reconstruction for trajectory classification

Trajectory classification has been extensively investigated in recent years, however, the problems about automatically modeling unlabeled and incomplete trajectories are far from being solved. In this paper, we propose a Cluster-based Dictionary Learning (CDL) approach that firstly constructs an initial cluster-based dictionary by K-means clustering and incrementally updates by exploring the importance of the label consistency constraint and classification optimization. Finally, a multiple-category classifier for trajectory is obtained with Locality-constrained Sparse Reconstruction (LSR) that combines both sparsity and local adaptability for robust trajectory classification. Experimental results show that our approach outperforms several recent approaches.