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- W4206419546 abstract "Minimizing traffic accidents between vehicles and pedestrians is of great importance in constructing intelligent transportation systems. Pedestrian behavior prediction is one of the major solutions to achieve this goal. The current methods are all extracting key information in the two-dimensional plane of image (e.g., the scene map around pedestrians, semantic map), and there is no specific use of the unique information in the three-dimensional space. Besides, the relative distance information that accounts for the interaction between the target pedestrian and the scene has not been properly utilized, and this information is missing in the current behavioral benchmark dataset. To solve these challenges, we introduce a new view for pedestrian intention prediction. The distance from each pixel to the camera is firstly estimated by the method of monocular depth estimation, so that the two-dimensional pixels are remapped to the three-dimensional space. Then, a new deep learning variant model is proposed to adequately fuse information from different perspectives. In particular, the experiments based on large-scale traffic dataset JAAD [1] and PIE [2] show that the multi-view architecture has an outstanding performance than state-of-the-art (SOTA) methods." @default.
- W4206419546 created "2022-01-26" @default.
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- W4206419546 date "2021-10-29" @default.
- W4206419546 modified "2023-09-25" @default.
- W4206419546 title "Pedestrian Intention Prediction via Depth Augmented Scene Restoration" @default.
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- W4206419546 doi "https://doi.org/10.1109/cvci54083.2021.9661140" @default.
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