Name
Title | ||
|---|---|---|
ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems. |
Abstract | ||
|---|---|---|
In this paper, we present deep residual network (ResNet)-based vehicle classification and localization methods using real traffic surveillance recordings. We utilize a MIOvision traffic dataset, which comprises 11 categories including a variety of vehicles, such as bicycle, bus, car, motorcycle, and so on. To improve the classification performance, we exploit a technique called joint fine-tuning (JF). In addition, we propose a dropping convolutional neural network (DropCNN) method to create a synergy effect with the JR For the localization, we implement basic concepts of state-of-the-art region based detector combined with a backbone convolutional feature extractor using 50 and 101 layers of residual networks and ensemble them into a single model. Finally, we achieved the highest accuracy in both classification and localization tasks using the dataset among several state-of-the-art methods, including VGG16, AlexNet, and ResNet50 for the classification, and YOLO Faster R-CNN, and SSD for the localization reported on the website. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CVPRW.2017.129 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | Volume |
Residual,Computer vision,Data mining,Pattern recognition,Computer science,Exploit,Feature extraction,Extractor,Artificial intelligence,Residual neural network,Detector | Conference | 2017 |
Issue | ISSN | Citations |
1 | 2160-7508 | 5 |
PageRank | References | Authors |
0.42 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heechul Jung | 1 | 208 | 10.24 |
| Min-Kook Choi | 2 | 19 | 3.47 |
| Ji-Hun Jung | 3 | 5 | 3.12 |
| Jinhee Lee | 4 | 80 | 21.11 |
| Soon Kwon | 5 | 26 | 5.77 |
| Woo Young Jung | 6 | 49 | 7.39 |