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ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems

Title
ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems
Authors
Jung, HeechulChoi, Min-KookJung, JihunLee, Jin-HeeKwon, SoonJung, Woo Young
DGIST Authors
Jung, Heechul; Choi, Min-Kook; Jung, Jihun; Lee, Jin-Hee; Kwon, SoonJung, Woo Young
Issue Date
2017-07-22
Citation
30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017, 934-940
Type
Conference
ISSN
2160-7508
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 CNN (DropCNN) method to create a synergy effect with the JF. 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. © 2017 IEEE.
URI
http://hdl.handle.net/20.500.11750/4761
DOI
10.1109/CVPRW.2017.129
Publisher
IEEE Computer Society
Related Researcher
  • Author Jung, Woo Young  
  • Research Interests Artificial Intelligence, Machine Learning, Autonomous Driving
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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