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• W4285127146 abstract "Pedestrian detection has wide applications in intelligent transportation. It is essential to understand pedestrian’s position and action instantaneously for autonomous driving. Most algorithms divide these tasks into sequential procedures where pedestrians are detected from shape-based features in video frames, and their behaviors are analyzed with frame tracking. Different from those, this work introduces a deep learning-based pedestrian detection method that only uses motion cues. The pedestrian motion, which is much different from that of static background and dynamic vehicles, is investigated in the spatial-temporal domain. The pedestrian leg movement forms a chain-type trace in the motion profile images even if the ego-vehicle moves. Instead of modeling walking actions based on kinematics, the chain structure is directly learned from a large pedestrian dataset in driving videos. This method works for the more challenging scenes observed on moving vehicles than those scenes from static cameras. The aim is to detect not only pedestrians promptly but also predict their walking direction in the driving space. Since a video is reduced to temporal images, real-time performance is achieved with a high mean average precision and a low false-positive rate on a publicly available dataset." @default.
• W4285127146 created "2022-07-14" @default.
• W4285127146 creator A5035028527 @default.
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• W4285127146 date "2023-02-01" @default.
• W4285127146 modified "2023-10-17" @default.
• W4285127146 title "DeepStep: Direct Detection of Walking Pedestrian From Motion by a Vehicle Camera" @default.
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• W4285127146 doi "https://doi.org/10.1109/tiv.2022.3186962" @default.
• W4285127146 hasPublicationYear "2023" @default.
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