ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems

• Published in: 2017 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
• Language: English
• Published: IEEE 01.07.2017
• Subjects: Computer vision; Conferences; Feature extraction; Pattern recognition; Proposals
• DOI: 10.1109/CVPRW.2017.129

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.