View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network
This provides an automated solution for vehicle identification, useful in applications like surveillance or traffic monitoring, but appears incremental as it builds on existing CNN approaches.
The paper tackles vehicle make, model, and color recognition by developing a computationally inexpensive convolutional neural network trained on millions of images, achieving state-of-the-art results and outperforming other methods on multiple benchmarks.
This paper describes the details of Sighthound's fully automated vehicle make, model and color recognition system. The backbone of our system is a deep convolutional neural network that is not only computationally inexpensive, but also provides state-of-the-art results on several competitive benchmarks. Additionally, our deep network is trained on a large dataset of several million images which are labeled through a semi-automated process. Finally we test our system on several public datasets as well as our own internal test dataset. Our results show that we outperform other methods on all benchmarks by significant margins. Our model is available to developers through the Sighthound Cloud API at https://www.sighthound.com/products/cloud