Search

Vehicle logo recognition (VLR) is a main issue in vehicle identification system. Logo recognition is still a challenge technique since VLR methods suffer from the large within-class variations due to the different illumination conditions, different viewpoints et al. In this paper, motivated by the excellent performance of the collaborative representation based classification (CRC), we formulate VLR problem under CRC scheme. It is noted that the performance of CRC is generally proportional to the size of the dictionary for better data representation capability. However, a large dictionary requires high computational cost. Aiming at maintaining the CRC performance but reducing the cost, the principal components analysis (PCA) is firstly employed on the class-dictionary to remove within-class information redundancy and noisy components. In addition, we introduce a new idea to code a data over a local dictionary instead of a global dictionary used in a conventional CRC, where the local dictionary is built by selecting the K-nearset neighbors of this data. As a result, a novel locally collaborative representation based classification with principal components (termed as LCRC_PC) method is systematically derived. The proposed LCRC_PC method is evaluated on two data sets. The average accuracies are 99.44% and 99.53% on a self-built data set and a public data set, respectively. Moreover, the computational cost of LCRC_PC is about a tenth of that of conventional CRC. Experimental results validate the effectiveness and robustness of our proposed LCRC_PC method.

Big traffic data analysis for intelligent transportation is attracting more and more attention. Due to different designs of vehicles in the same class and the similarity of shape and textures between different classes, vehicle classification is remaining a challenge. In this paper, different from traditional methods that only classify vehicles to two or three types in one viewpoint, a novel method using local and structural features has been proposed for vehicle classification in real-time traffic system that has a good ability to categorize vehicles into more specific types and is robust to the changes in viewpoint. Specifically, local features are obtained using scale invariant feature transform (SIFT), and an efficient L2-norm sparse coding technique is used to reduce computational cost. Besides, vehicle building structures are extracted as structural features. Finally, linear support vector machine (SVM) is used as the classifier. The performance evaluations using real vehicle images extracted from surveillance videos in different viewpoints are carried out and five vehicle classes (SUV, truck, van, bus, car) are considered. Experimental results show that the proposed method can obtain an average accuracy of 95.95% in real-time, which validate the effectiveness of our method.