Data Augmentation and Clustering for Vehicle Make/Model Classification
This work addresses vehicle re-identification in Intelligent Traffic Systems, but it is incremental as it builds on existing ResNet architectures with specific enhancements.
The paper tackled vehicle make/model classification by using data augmentation and clustering to improve robustness and classification results, achieving improved performance with concrete gains from bias removal and centroid normalization.
Vehicle shape information is very important in Intelligent Traffic Systems (ITS). In this paper we present a way to exploit a training data set of vehicles released in different years and captured under different perspectives. Also the efficacy of clustering to enhance the make/model classification is presented. Both steps led to improved classification results and a greater robustness. Deeper convolutional neural network based on ResNet architecture has been designed for the training of the vehicle make/model classification. The unequal class distribution of training data produces an a priori probability. Its elimination, obtained by removing of the bias and through hard normalization of the centroids in the classification layer, improves the classification results. A developed application has been used to test the vehicle re-identification on video data manually based on make/model and color classification. This work was partially funded under the grant.