Person Re-Identification on a Mobile Robot Using a Depth Camera

Abstract

In this paper, we designed and implemented a real-time person re-identification API on a mobile robot, for a closed-and open-world setting, using only the IR gray value image of a depth camera. Since common datasets are not usable we created our own dataset using the IR gray value images, the pose and image processing techniques. Then we trained the state-of-the-art neural network for person re-identification with common parameters and methods. For running it in real-time, we sped up the model as well as the application. It is possible to re-identify three persons at once, at around 10 FPS. Our model reaches as closed-world setting a rank-1-accuracy of 95.5 %. With an additional threshold, coming from rank-1-accuracy of closed-world setting, our real-time application reaches as open-world setting a f'l-score of 79.44 % and a recall of 68.44 %.