Vehicle verification in two nonoverlapped views using sparse representation

Vehicle verification in two different views can be applied for Intelligent Transportation System. However, object appearance matching in two different views is difficult. The vehicle images captured in two views are represented as a feature pair which can be classified as the same/different pair. Sparse representation (SR) has been applied for reconstruction, recognition, and verification. However, the SR dictionary may not guarantee feature sparsity and effective representation. In the paper, we propose Boost-KSVD method without using initial random atom to generate the SR dictionary which can be applied for object verification with very good accuracy. Then, we develop a discriminative criterion to decide the SR dictionary size. Finally, the experiments show that our method can generate better verification accuracy compared with the other methods.