Optimization of Image Preprocessing and Background Influences using a Depth Camera for Person Re-Identification on a Mobile Robot

In this paper, we optimize datasets with different image preprocessing techniques for person re-identification using the ResNet18 model on a mobile robot with limited hardware, e.g., computational power and depth camera. For this, we create 16 datasets for which we discovered that the inverted original images, from the IR gray value images of the depth camera, results in the highest values with an r1, r5 and mAP of 98.48 %,99.82 % and 80.86 %. Additionally, we explore the cross-dataset evaluation for the 16 datasets to examine the robustness of our model, which points to a low generalizability. The scores are associated with the similarity between the trained and evaluated dataset.