ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems

In this paper, we present 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 c...

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Bibliographic Details
Published in2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) pp. 934 - 940
Main Authors Heechul Jung, Min-Kook Choi, Jihun Jung, Jin-Hee Lee, Soon Kwon, Woo Young Jung
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2017
Subjects
Computer vision
Conferences
Feature extraction
Pattern recognition
Proposals
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Summary:In this paper, we present 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.
ISSN:2160-7516
DOI:10.1109/CVPRW.2017.129