In past years, the task of automatic vehicle trajectory analysis in video surveillance systems has gained increasing attention in the research community. Vehicle trajectory analysis can identify normal and abnormal vehicle motion patterns and is useful for traffic management. Although some analysis methods of vehicle trajectory have been developed, the application of these methods is still limited in practice. In this study, a novel adaptive vehicle trajectory classification method via sparse reconstruction and mutual information analysis based on video surveillance systems was proposed. The l0-norm minimization of sparse reconstruction in the method was relaxed to the lp-norm minimization (0 < p < 1). In addition, to consider the nonlinear correlation between the test trajectory and the dictionary, mutual information between the test trajectory and the reconstructed one was taken into account. A hybrid orthogonal matching pursuit–Newton method (HON) was developed to effectively find the sparse solutions for trajectory classification. Two real-world data sets (including the stop sign data set and straight data set) were used in the experiments to validate the performance and effectiveness of the proposed method. Experimental results show that the trajectory classification accuracy is significantly improved by the proposed method compared with most well-known classifiers, namely, NB, k–nearest neighbor, support vector machine, and typical extant sparse reconstruction methods.
TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification
