Name
Title | ||
|---|---|---|
View Independent Vehicle Make, Model and Color Recognition Using Convolutional Neural Network. |
Abstract | ||
|---|---|---|
This paper describes the details of Sighthoundu0027s 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 this https URL |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Pattern recognition,Computer science,Convolutional neural network,Color recognition,Artificial intelligence,Machine learning,Cloud computing |
DocType | Volume | Citations |
Journal | abs/1702.01721 | 4 |
PageRank | References | Authors |
0.53 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Afshin Dehghan | 1 | 652 | 19.57 |
| Syed Zain Masood | 2 | 92 | 5.80 |
| Guang Shu | 3 | 12 | 1.69 |
| Enrique G. Ortiz | 4 | 180 | 6.15 |