Multi-task hybrid dictionary learning for vehicle classification in sensor networks

In this article, we propose a novel multi-task hybrid dictionary learning approach for moving vehicle classification tasks using multi-sensor networks to improve the classification accuracy in complex scenes with low time complexity, which considers both correlations and complementary information among multiple heterogeneous sensors simultaneously to learn a hybrid dictionary within observations of each sensor. The efficient hybrid dictionary consists of a synthesis dictionary and an analysis dictionary, where discriminative codes can be generated by the trained analysis dictionary and class-specific discriminative reconstruction can be achieved by the trained synthesis dictionary. Extensive experiments are conducted on real data sets captured by the multiple heterogeneous sensors, and the results demonstrate that the proposed method can use the multi-feature fusion method to improve the vehicle classification accuracy, and it can learn a hybrid dictionary to make sure that the sparse coding matrix is obtained by simple linear mapping function. Moreover, the problem of ℓ p -norm ( p ⩽ 1 ) sparse coding can been solved, to reduce the time complexity of this algorithm, compared with support vector machine, sparse representation classification, label consistent KSVD, Fisher discrimination dictionary learning, hybrid dictionary learning, multi-task sparse representation classification, and multi-task Fisher discrimination dictionary learning algorithms.