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Cross-Modal Distillation for RGB-Depth Person Re-Identification
[article]
2022
arXiv
pre-print
Person re-identification is a key challenge for surveillance across multiple sensors. Prompted by the advent of powerful deep learning models for visual recognition, and inexpensive RGB-D cameras and sensor-rich mobile robotic platforms, e.g. self-driving vehicles, we investigate the relatively unexplored problem of cross-modal re-identification of persons between RGB (color) and depth images. The considerable divergence in data distributions across different sensor modalities introduces
arXiv:1810.11641v3
fatcat:lwrhwae66vgsbnj3rvtlcdl63a