Car Type Recognition with Deep Neural Networks
This is an incremental improvement for applications in automotive or surveillance systems.
The paper tackled car type recognition (Bus, Truck, Van, Small car) using deep neural networks and SVMs, achieving over 97% accuracy on a dataset of 6500 images, outperforming earlier methods with manual feature extraction.
In this paper we study automatic recognition of cars of four types: Bus, Truck, Van and Small car. For this problem we consider two data driven frameworks: a deep neural network and a support vector machine using SIFT features. The accuracy of the methods is validated with a database of over 6500 images, and the resulting prediction accuracy is over 97 %. This clearly exceeds the accuracies of earlier studies that use manually engineered feature extraction pipelines.