Name
Abstract | ||
|---|---|---|
Traffic surveillance has always been a challenging task to automate. The main difficulties arise from the high variation of the vehicles appertaining to the same category, low resolution, changes in illumination and occlusions. Due to the lack of large labeled datasets, deep learning techniques still have not shown their full potential. In this paper, thanks to the MIOvision Traffic Camera Dataset (MIO-TCD), an Ensemble of Deep Networks (EDeN) is used to successfully classify surveillance images into eleven different classes of vehicles. The ensemble of deep networks consists of 2 individual networks that are trained independently. Extensive evaluations were carried out using individual networks and their ensemble, using the MIO-TCD dataset that consists of 786,702 diverse images resembling a real-world environment. Experimental results show that the ensemble of networks gives better performance compared to individual networks and it is robust to noise. The ensemble of networks achieves an accuracy of 97.80%, mean precision of 94.39%, mean recall of 91.90% and Cohen kappa of 96.58. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPRW.2017.125 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | Volume |
Computer vision,Radar tracker,Pattern recognition,Computer science,Cohen's kappa,Artificial intelligence,Deep learning,Recall,Machine learning | Conference | 2017 |
Issue | ISSN | Citations |
1 | 2160-7508 | 1 |
PageRank | References | Authors |
0.36 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rajkumar Theagarajan | 1 | 4 | 2.76 |
| Federico Pala | 2 | 46 | 3.53 |
| Bir Bhanu | 3 | 3356 | 380.19 |