  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 representation space of person's appearance in both RGB and
depth images. In addition, we propose a cross-modal attention mechanism where
the gating signal from one modality can dynamically activate the most
discriminant CNN filters of the other modality. The proposed distillation
method is compared to conventional and deep learning approaches proposed for
other cross-domain re-identification tasks. Results obtained on the public BIWI
and RobotPKU datasets indicate that the proposed method can significantly
outperform the state-of-the-art approaches by up to 16.1% in mean Average
Precision (mAP), demonstrating the benefit of the distillation paradigm. The
experimental results also indicate that using cross-modal attention allows to
improve recognition accuracy considerably with respect to the proposed
distillation method and relevant state-of-the-art approaches.
