Cross-Modal Distillation for RGB-Depth Person Re-Identification

Cross-Modal Distillation for RGB-Depth Person Re-Identification

Frank Hafner, Amran Bhuiyan, Julian F. P. Kooij, Eric Granger

Computer Vision and Image Understanding 2022/1/4

Abstract : 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 additional challenges to the typical difficulties like distinct viewpoints, occlusions, and pose and illumination variation. While some work has investigated re-identification across RGB and infrared, we take inspiration from successes in transfer learning from RGB to depth in object detection tasks. Our main contribution is a novel method for cross-modal distillation for robust person re-identification, which learns a shared feature …