PCANet-Based Convolutional Neural Network Architecture for a Vehicle Model Recognition System

Abstract

Vehicle model recognition plays a crucial role in intelligent transportation systems. Most of the existing vehicle model recognition methods focus on locating a large global feature or extracting more than one local subordinate-level feature from a vehicle image. In this paper, we propose the principal component analysis network-based convolutional neural network (PCNN) and pinpoint only one discriminative local feature of a vehicle, which is the vehicle headlamp, for vehicle model recognition. The proposed model eliminates the need for locating and segmenting the headlamp precisely. In particular, PCNN ascertains the effectiveness of both principal component analysis and CNN in extracting hierarchical features from a vehicle headlamp image and also reducing the computational complexity of the traditional CNN system. To further enhance the training procedure while still keeping the discriminative property of the network, the fully connected layer is updated by backpropagation optimized with stochastic gradient descent. The proposed method is validated using a data set that comprises 13 300 training images and 2660 testing images, respectively. The model is robust against various distortions. Experiments show that PCNN outperforms state-of-the-art techniques with an average accuracy of 99.51% over 38 vehicle makes and models using the PLUS data set. In addition, the effectiveness of the proposed method is also validated using the public CompCars data set, achieving 89.83% accuracy over 357 vehicle models.