Locality-Constrained Sparse Reconstruction for Trajectory Classification
Ce Li, Zhenjun Han, Qixiang Ye, Shan Gao, Lijin Pang, Jianbin Jiao · 2014
Trajectory classification has been extensively investigated in recent years, however, problems remain when processing incomplete trajectories of noises and local variations. In this paper, we propose a Locality-constrained Sparse Reconstruction (LSR) approach that explores both sparsity and local adaptability for robust trajectory classification. A trajectory dictionary with locality constrains is constructed with track lets partitioned from collected trajectories by control points of cubic B-spline curves. On the dictionary, the proposed LSR is used to calculate a discriminate code matrix. Then, a loss weighted decoding strategy is employed to perform multi-class trajectory classification. In addition, the approach can be used for anomalous trajectory detection with a thresholding strategy. Experiments on two datasets show that the results of the LSR approach improve the state of the art.