Nov. 23, 2022, 2:15 a.m. | Hansi Liu, Kristin Dana, Marco Gruteser, Hongsheng Lu

cs.CV updates on arXiv.org arxiv.org

In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians'
accurate locations is crucial to traffic safety. Current systems adopt cameras
and wireless sensors to detect and estimate people's locations via sensor
fusion. Standard fusion algorithms, however, become inapplicable when
multi-modal data is not associated. For example, pedestrians are out of the
camera field of view, or data from camera modality is missing. To address this
challenge and produce more accurate location estimations for pedestrians, we
propose a Generative Adversarial Network …

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