Vehicle Identification Via Sparse Representation

Abstract

In this paper, we propose a system using video cameras to perform vehicle identification. We tackle this problem by reconstructing an input by using multiple linear regression models and compressed sensing, which provide new ways to deal with three crucial issues in vehicle identification, namely, feature extraction, online vehicle identification database buildup , and robustness to occlusions and misalignment. The results show the capability of the proposed approach.