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학술대회 TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification
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오지용, 임길택, 정윤수
International Conference on Pattern Recognition Applications and Methods (ICPRAM) 2021, pp.414-422
20ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
Vehicle trajectory classification plays an important role in intelligent transportation systems because it can be utilized in traffic flow estimation at an intersection and anomaly detection such as traffic accidents and violations of traffic regulations. In this paper, we propose a new neural network architecture for vehicle trajectory classification by modifying the PointNet architecture, which was proposed for point cloud classification and semantic segmentation. The modifications are derived based on analyzing the differences between the properties of vehicle trajectory and point cloud. We call the modified network TrajNet. It is demonstrated from experiments using three public datasets that TrajNet can classify vehicle trajectories faster and more slightly accurate than the conventional networks used in the previous studies.
Deep neural network, Intelligent transportation system, TrajNet, Vehicle trajectory classification
KSP 제안 키워드
Deep neural network(DNN), Intelligent Transport Systems(ITS), Point cloud classification, Public Datasets, Semantic segmentation, Traffic accident, Vehicle trajectory, anomaly detection, intelligent transportation, neural network architecture, traffic flow estimation
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저작자 표시 - 비영리 - 변경금지 (CC BY NC ND)